Scheduled Commit

data/clustering_battle-0325b067-7cac-407c-9ffa-76d99f10c9f7.jsonl

@@ -1,2 +1 @@
 {"tstamp": 1722263906.0355, "task_type": "clustering", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "53aecd7e5dc24a6cab610e361d69ec88", "0_model_name": "voyage-multilingual-2", "0_prompt": ["Pikachu", "Darth Vader", "Yoda", "Slugma", "Gandalf", "Legolas"], "0_ncluster": 3, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "b2a92da0d4b64569af45351deee84e7f", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": ["Pikachu", "Darth Vader", "Yoda", "Slugma", "Gandalf", "Legolas"], "1_ncluster": 3, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
-{"tstamp": 1722264058.1129, "task_type": "clustering", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "8320a5246e234aeeaa0d5ca7c4a9aed1", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": ["Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas", "Mickey Mouse", "Donald Duck", "Charizard"], "0_ncluster": 4, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "2c8225553fcc4e4fb2cebb7679a345c8", "1_model_name": "GritLM/GritLM-7B", "1_prompt": ["Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas", "Mickey Mouse", "Donald Duck", "Charizard"], "1_ncluster": 4, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}

data/clustering_battle-115786ef-c6bb-4ff6-b66a-85511fd72c53.jsonl

@@ -1,4 +1,3 @@
 {"tstamp": 1722281965.6179, "task_type": "clustering", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "5accf948c7ac40c89eeb322a4e13bf62", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "0_ncluster": 2, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "44a299f8e8f74b61a7800197c1b479ab", "1_model_name": "intfloat/multilingual-e5-large-instruct", "1_prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "1_ncluster": 2, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
 {"tstamp": 1722282005.3598, "task_type": "clustering", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "f8293714822c4dd3a8248660500f9035", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": ["Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas", "Mickey Mouse", "Donald Duck", "Charizard"], "0_ncluster": 4, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "336180d1bf524d83a68ce1cc38470459", "1_model_name": "voyage-multilingual-2", "1_prompt": ["Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas", "Mickey Mouse", "Donald Duck", "Charizard"], "1_ncluster": 4, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
-{"tstamp": 1722282066.8359, "task_type": "clustering", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "a5e394365c244068b232bddcafe5199b", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": ["China'\u0080\u0099s statistics chief Wang Baoan detained in graft investigation", "NSFW: Wuhan man arrested by police for live-streaming his midnight lovemaking session", "Planetary Yagya ,Graha Shanti Yagya Bring Out Happiness in Your Life -astrodevam.com", "I am searching a particular type of gel/resin"], "0_ncluster": 3, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "308dd4a8eab841c7a29837dc131fdfe8", "1_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "1_prompt": ["China'\u0080\u0099s statistics chief Wang Baoan detained in graft investigation", "NSFW: Wuhan man arrested by police for live-streaming his midnight lovemaking session", "Planetary Yagya ,Graha Shanti Yagya Bring Out Happiness in Your Life -astrodevam.com", "I am searching a particular type of gel/resin"], "1_ncluster": 3, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
 {"tstamp": 1722282151.7917, "task_type": "clustering", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "2a1a8ba50b6d4bc0b2ba7c0abd8dba81", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": ["Transits \u2013 Neptune conjunct the Moon | LUA ASTROLOGY", "Get Ex Love Back - Swami Rishinand", "Does the Civic Si have any competition?", "MARIN MARAIS (1656-1728) - Tombeau pour Mr. de Lully", "What instrument should I learn?", "David Lang - Justs (After the Song of Songs)", "Tips to live desire life with astrology | India"], "0_ncluster": 3, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "1636fa4a9c774083b13a62e6f18461cc", "1_model_name": "voyage-multilingual-2", "1_prompt": ["Transits \u2013 Neptune conjunct the Moon | LUA ASTROLOGY", "Get Ex Love Back - Swami Rishinand", "Does the Civic Si have any competition?", "MARIN MARAIS (1656-1728) - Tombeau pour Mr. de Lully", "What instrument should I learn?", "David Lang - Justs (After the Song of Songs)", "Tips to live desire life with astrology | India"], "1_ncluster": 3, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}

data/clustering_battle-1fa504af-967a-4ff8-a6ff-4abf5fbdd1c5.jsonl

@@ -1,3 +1,2 @@
 {"tstamp": 1722266426.4606, "task_type": "clustering", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "8231665ecd594c86b9bee0001d5e989c", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": ["Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas", "Mickey Mouse", "Donald Duck", "Charizard"], "0_ncluster": 4, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "c2047173c4764d6ebfaf1db67e37ed7d", "1_model_name": "GritLM/GritLM-7B", "1_prompt": ["Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas", "Mickey Mouse", "Donald Duck", "Charizard"], "1_ncluster": 4, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
 {"tstamp": 1722266465.3044, "task_type": "clustering", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "e40f45b38d8d4568a17340ab5c831eda", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": ["Tesla\u2019s Model 3 will boast a 200-mile or greater range, cost $35,000", "How to Buy an Android Car DVD with GPS Navigation?", "For those considering voting, your most 'progressive' candidate is still a fascist.", "She's plotting her revenge guys, help!", "Bandar Bola - Apa Sucker Akan Beli Obligasi Lotus Esprit Submarine Untuk $ 1.000.000?", "Durch Die Nacht Mit... Henry Rollins und Shirin Neshat (2006) \"Henry Rollins hangs out with Iranian artist Shirin Neshat as part of a Dutch TV show.\"", "Black Lives Matter protesters block San Francisco's Bay Bridge", "Ceiling Cat vs. Basement Cat; Arial Image from Syria Shows Biblical Struggle", "\u201cAllure\u201d by any other name: The double standards of rape culture, racism, and gender in pop music", "New Zealand's Transgender Prisoners Fear Double Bunking Will Lead to More Rape", "Homeless return to Sacramento City Hall under political, legal cloud", "Meet Rogan and Bosley, two senior grumpy Persian brothers"], "0_ncluster": 3, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "e7b78554b50d469abeebf74e9aef95f0", "1_model_name": "text-embedding-3-large", "1_prompt": ["Tesla\u2019s Model 3 will boast a 200-mile or greater range, cost $35,000", "How to Buy an Android Car DVD with GPS Navigation?", "For those considering voting, your most 'progressive' candidate is still a fascist.", "She's plotting her revenge guys, help!", "Bandar Bola - Apa Sucker Akan Beli Obligasi Lotus Esprit Submarine Untuk $ 1.000.000?", "Durch Die Nacht Mit... Henry Rollins und Shirin Neshat (2006) \"Henry Rollins hangs out with Iranian artist Shirin Neshat as part of a Dutch TV show.\"", "Black Lives Matter protesters block San Francisco's Bay Bridge", "Ceiling Cat vs. Basement Cat; Arial Image from Syria Shows Biblical Struggle", "\u201cAllure\u201d by any other name: The double standards of rape culture, racism, and gender in pop music", "New Zealand's Transgender Prisoners Fear Double Bunking Will Lead to More Rape", "Homeless return to Sacramento City Hall under political, legal cloud", "Meet Rogan and Bosley, two senior grumpy Persian brothers"], "1_ncluster": 3, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
-{"tstamp": 1722266480.7581, "task_type": "clustering", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "03d7d22aca0c4ecca15bcf433638a302", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": ["Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas", "Mickey Mouse", "Donald Duck", "Charizard"], "0_ncluster": 4, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "1c053f39785540928673118b8d40060b", "1_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "1_prompt": ["Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas", "Mickey Mouse", "Donald Duck", "Charizard"], "1_ncluster": 4, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}

data/clustering_battle-5ff75294-3b96-4e61-b91f-99fe468ad573.jsonl

@@ -2,14 +2,11 @@
 {"tstamp": 1722276997.8264, "task_type": "clustering", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "bd490836af8e4370bf9ffcb7a54ba754", "0_model_name": "voyage-multilingual-2", "0_prompt": ["Piano", "Electron", "Sushi", "Violin", "Proton", "Pasta", "Trumpet", "Neutron", "Steak", "Clarinet", "Quark", "Salad", "Harp", "Photon", "Soup", "Cello", "Neutrino", "Sandwich", "Piano", "Electron", "Sushi", "Violin", "Proton", "Pasta", "Trumpet", "Neutron", "Steak", "Clarinet", "Quark", "Salad", "Harp", "Photon", "Soup", "Cello", "Neutrino", "Sandwich"], "0_ncluster": 3, "0_output": "", "0_ndim": "2D (press for 3D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "3b19aa95ee4f4908bdf23aec8478568a", "1_model_name": "embed-english-v3.0", "1_prompt": ["Piano", "Electron", "Sushi", "Violin", "Proton", "Pasta", "Trumpet", "Neutron", "Steak", "Clarinet", "Quark", "Salad", "Harp", "Photon", "Soup", "Cello", "Neutrino", "Sandwich", "Piano", "Electron", "Sushi", "Violin", "Proton", "Pasta", "Trumpet", "Neutron", "Steak", "Clarinet", "Quark", "Salad", "Harp", "Photon", "Soup", "Cello", "Neutrino", "Sandwich"], "1_ncluster": 3, "1_output": "", "1_ndim": "2D (press for 3D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
 {"tstamp": 1722277187.8827, "task_type": "clustering", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "f8c4bf85d67f41de9b8bbfa14f7ebd1b", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": ["Piano", "Electron", "Sushi", "Violin", "Proton", "Pasta", "Trumpet", "Neutron", "Steak", "Clarinet", "Quark", "Salad", "Harp", "Photon", "Soup", "Cello", "Neutrino", "Sandwich", "Piano", "Electron", "Sushi", "Violin", "Proton", "Pasta", "Trumpet", "Neutron", "Steak", "Clarinet", "Quark", "Salad", "Harp", "Photon", "Soup", "Cello", "Neutrino", "Sandwich"], "0_ncluster": 3, "0_output": "", "0_ndim": "2D (press for 3D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "1d4dc82e83524d199f681f7dd61a25f3", "1_model_name": "text-embedding-004", "1_prompt": ["Piano", "Electron", "Sushi", "Violin", "Proton", "Pasta", "Trumpet", "Neutron", "Steak", "Clarinet", "Quark", "Salad", "Harp", "Photon", "Soup", "Cello", "Neutrino", "Sandwich", "Piano", "Electron", "Sushi", "Violin", "Proton", "Pasta", "Trumpet", "Neutron", "Steak", "Clarinet", "Quark", "Salad", "Harp", "Photon", "Soup", "Cello", "Neutrino", "Sandwich"], "1_ncluster": 3, "1_output": "", "1_ndim": "2D (press for 3D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
 {"tstamp": 1722277210.6836, "task_type": "clustering", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "adafc83b33cc4355aadf6fc9e10cdbcd", "0_model_name": "GritLM/GritLM-7B", "0_prompt": ["Piano", "Electron", "Sushi", "Violin", "Proton", "Pasta", "Trumpet", "Neutron", "Steak", "Clarinet", "Quark", "Salad", "Harp", "Photon", "Soup", "Cello", "Neutrino", "Sandwich"], "0_ncluster": 3, "0_output": "", "0_ndim": "2D (press for 3D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "67de54733b554dae8eb10a3fe142bde8", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_prompt": ["Piano", "Electron", "Sushi", "Violin", "Proton", "Pasta", "Trumpet", "Neutron", "Steak", "Clarinet", "Quark", "Salad", "Harp", "Photon", "Soup", "Cello", "Neutrino", "Sandwich"], "1_ncluster": 3, "1_output": "", "1_ndim": "2D (press for 3D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
-{"tstamp": 1722277432.4493, "task_type": "clustering", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "5459804ea04147b2ab86aa6b5f4eb229", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "0_ncluster": 4, "0_output": "", "0_ndim": "2D (press for 3D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "c31279469df84748acc2819f595c1a05", "1_model_name": "voyage-multilingual-2", "1_prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "1_ncluster": 4, "1_output": "", "1_ndim": "2D (press for 3D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
 {"tstamp": 1722277499.9324, "task_type": "clustering", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "0c3b0361b0f14423ae0b8081f781a2ec", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin", "Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "0_ncluster": 4, "0_output": "", "0_ndim": "2D (press for 3D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "6522beccc4b44fffb049889a46d4ecd8", "1_model_name": "text-embedding-004", "1_prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin", "Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "1_ncluster": 4, "1_output": "", "1_ndim": "2D (press for 3D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
 {"tstamp": 1722277520.8821, "task_type": "clustering", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "7d33a06aa1a24155b8c2128567b41440", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "0_ncluster": 4, "0_output": "", "0_ndim": "2D (press for 3D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "4d7cdf865a6141ff9a0097f1840b007c", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "1_ncluster": 4, "1_output": "", "1_ndim": "2D (press for 3D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
-{"tstamp": 1722277586.3169, "task_type": "clustering", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "109315a8077f467fbbe89301b167d988", "0_model_name": "Salesforce/SFR-Embedding-2_R", "0_prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "0_ncluster": 4, "0_output": "", "0_ndim": "2D (press for 3D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "8b2a63efc1364029bdee4cabc7bcc79e", "1_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "1_prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "1_ncluster": 4, "1_output": "", "1_ndim": "2D (press for 3D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
 {"tstamp": 1722277608.0973, "task_type": "clustering", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "b5be4399c1d3491487992d132ed1ba24", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "0_ncluster": 4, "0_output": "", "0_ndim": "2D (press for 3D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "66410239cb954ef282b1131aaf0aba1b", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "1_ncluster": 4, "1_output": "", "1_ndim": "2D (press for 3D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
 {"tstamp": 1722277634.607, "task_type": "clustering", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "1ec040e83c874eb8904ba54ab7663eb1", "0_model_name": "GritLM/GritLM-7B", "0_prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "0_ncluster": 4, "0_output": "", "0_ndim": "2D (press for 3D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "04dd49e251324f79b5106e940dc1babc", "1_model_name": "text-embedding-004", "1_prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "1_ncluster": 4, "1_output": "", "1_ndim": "2D (press for 3D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
 {"tstamp": 1722277653.6919, "task_type": "clustering", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "c84d74b682b94723becd3460706e99de", "0_model_name": "Salesforce/SFR-Embedding-2_R", "0_prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "0_ncluster": 4, "0_output": "", "0_ndim": "2D (press for 3D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "1c575591f9084ce0ae92efaef9e08dc5", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "1_ncluster": 4, "1_output": "", "1_ndim": "2D (press for 3D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
-{"tstamp": 1722277672.6269, "task_type": "clustering", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "88864c80f832487898121865b1636a91", "0_model_name": "GritLM/GritLM-7B", "0_prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "0_ncluster": 4, "0_output": "", "0_ndim": "2D (press for 3D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "fb5d34913809493d932b99c9ef9c6ec8", "1_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "1_prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "1_ncluster": 4, "1_output": "", "1_ndim": "2D (press for 3D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
 {"tstamp": 1722277689.5573, "task_type": "clustering", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "f18d9dc29d89449e920ee22df252eba0", "0_model_name": "text-embedding-004", "0_prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "0_ncluster": 4, "0_output": "", "0_ndim": "2D (press for 3D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "6f854fced7744423a11bfab80d2d5dbf", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "1_ncluster": 4, "1_output": "", "1_ndim": "2D (press for 3D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
 {"tstamp": 1722277704.7607, "task_type": "clustering", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "0b12d1048e4b439dbf8a9199bf4cb161", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "0_ncluster": 4, "0_output": "", "0_ndim": "2D (press for 3D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "934ba3aa2205475dad78dd1430ae4b58", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "1_ncluster": 4, "1_output": "", "1_ndim": "2D (press for 3D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
 {"tstamp": 1722277716.7904, "task_type": "clustering", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "5c021df99cca46e38b192e3f1080d0b6", "0_model_name": "GritLM/GritLM-7B", "0_prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "0_ncluster": 4, "0_output": "", "0_ndim": "2D (press for 3D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "2df6032d93b845668ba7a28c31e53f89", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "1_ncluster": 4, "1_output": "", "1_ndim": "2D (press for 3D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
@@ -21,7 +18,6 @@
 {"tstamp": 1722277923.6911, "task_type": "clustering", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "a6bd45de40d741ef88a4c79583520af4", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": ["Mars", "Soccer", "Copper", "Democracy", "Jupiter", "Basketball", "Silver", "Monarchy", "Saturn", "Tennis", "Gold", "Oligarchy"], "0_ncluster": 4, "0_output": "", "0_ndim": "2D (press for 3D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "3cab612d49d044a2b684781558bf147d", "1_model_name": "text-embedding-3-large", "1_prompt": ["Mars", "Soccer", "Copper", "Democracy", "Jupiter", "Basketball", "Silver", "Monarchy", "Saturn", "Tennis", "Gold", "Oligarchy"], "1_ncluster": 4, "1_output": "", "1_ndim": "2D (press for 3D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
 {"tstamp": 1722277948.0387, "task_type": "clustering", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "e604f87fd389401b8cd78468144aefff", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": ["Mars", "Soccer", "Copper", "Democracy", "Jupiter", "Basketball", "Silver", "Monarchy", "Saturn", "Tennis", "Gold", "Oligarchy"], "0_ncluster": 4, "0_output": "", "0_ndim": "2D (press for 3D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "41b9a03eda0d40bbb9faee0c9ff4a408", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": ["Mars", "Soccer", "Copper", "Democracy", "Jupiter", "Basketball", "Silver", "Monarchy", "Saturn", "Tennis", "Gold", "Oligarchy"], "1_ncluster": 4, "1_output": "", "1_ndim": "2D (press for 3D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
 {"tstamp": 1722277970.6393, "task_type": "clustering", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "5f0d7b4f509f4da98db2b3e484edc498", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": ["Mars", "Soccer", "Copper", "Democracy", "Jupiter", "Basketball", "Silver", "Monarchy", "Saturn", "Tennis", "Gold", "Oligarchy"], "0_ncluster": 4, "0_output": "", "0_ndim": "2D (press for 3D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "2258ba02393e44cda398c929bde09289", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_prompt": ["Mars", "Soccer", "Copper", "Democracy", "Jupiter", "Basketball", "Silver", "Monarchy", "Saturn", "Tennis", "Gold", "Oligarchy"], "1_ncluster": 4, "1_output": "", "1_ndim": "2D (press for 3D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
-{"tstamp": 1722278276.5054, "task_type": "clustering", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "86bae053a0184844a9c3451a4fde105c", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": ["Mars", "Soccer", "Copper", "Democracy", "Jupiter", "Basketball", "Silver", "Monarchy", "Saturn", "Tennis", "Gold", "Oligarchy"], "0_ncluster": 4, "0_output": "", "0_ndim": "2D (press for 3D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "2024bf4252a34e8b8149a149f71e6ce7", "1_model_name": "text-embedding-004", "1_prompt": ["Mars", "Soccer", "Copper", "Democracy", "Jupiter", "Basketball", "Silver", "Monarchy", "Saturn", "Tennis", "Gold", "Oligarchy"], "1_ncluster": 4, "1_output": "", "1_ndim": "2D (press for 3D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
 {"tstamp": 1722278296.4392, "task_type": "clustering", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "ddd2aca4d50c4ac199a2a7838b4c2388", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": ["Mars", "Soccer", "Copper", "Democracy", "Jupiter", "Basketball", "Silver", "Monarchy", "Saturn", "Tennis", "Gold", "Oligarchy"], "0_ncluster": 4, "0_output": "", "0_ndim": "2D (press for 3D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "7a19184923fc4cd49e696614c04820b1", "1_model_name": "voyage-multilingual-2", "1_prompt": ["Mars", "Soccer", "Copper", "Democracy", "Jupiter", "Basketball", "Silver", "Monarchy", "Saturn", "Tennis", "Gold", "Oligarchy"], "1_ncluster": 4, "1_output": "", "1_ndim": "2D (press for 3D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
 {"tstamp": 1722278315.581, "task_type": "clustering", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "cf4d4d0347c0460892d49c23b3e91374", "0_model_name": "Salesforce/SFR-Embedding-2_R", "0_prompt": ["Mars", "Soccer", "Copper", "Democracy", "Jupiter", "Basketball", "Silver", "Monarchy", "Saturn", "Tennis", "Gold", "Oligarchy"], "0_ncluster": 4, "0_output": "", "0_ndim": "2D (press for 3D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "9789ba50c74140aaa4169b7059065832", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": ["Mars", "Soccer", "Copper", "Democracy", "Jupiter", "Basketball", "Silver", "Monarchy", "Saturn", "Tennis", "Gold", "Oligarchy"], "1_ncluster": 4, "1_output": "", "1_ndim": "2D (press for 3D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
 {"tstamp": 1722278356.4265, "task_type": "clustering", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "458ad88943e7400788711dfc6027e09e", "0_model_name": "embed-english-v3.0", "0_prompt": ["Mars", "Soccer", "Copper", "Democracy", "Jupiter", "Basketball", "Silver", "Monarchy", "Saturn", "Tennis", "Gold", "Oligarchy"], "0_ncluster": 4, "0_output": "", "0_ndim": "2D (press for 3D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "bb501830f53a48669dbcae28b4677590", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": ["Mars", "Soccer", "Copper", "Democracy", "Jupiter", "Basketball", "Silver", "Monarchy", "Saturn", "Tennis", "Gold", "Oligarchy"], "1_ncluster": 4, "1_output": "", "1_ndim": "2D (press for 3D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}

data/clustering_battle-79029e82-3905-4a19-8fd7-0e6319f51acd.jsonl

@@ -1,3 +1,2 @@
 {"tstamp": 1722282371.8069, "task_type": "clustering", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "92f1ddd406b34b1c9e9415d72da9f316", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": ["Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas", "Mickey Mouse", "Donald Duck", "Charizard"], "0_ncluster": 4, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "10957173cee142dbba2922f1af8b5a3b", "1_model_name": "embed-english-v3.0", "1_prompt": ["Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas", "Mickey Mouse", "Donald Duck", "Charizard"], "1_ncluster": 4, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
-{"tstamp": 1722282380.8471, "task_type": "clustering", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "d87bc59e4487439d8de7408701843f84", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": ["Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas", "Mickey Mouse", "Donald Duck", "Charizard"], "0_ncluster": 4, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "ef0d30af037f4c71b2fb04cc8a708fee", "1_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "1_prompt": ["Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas", "Mickey Mouse", "Donald Duck", "Charizard"], "1_ncluster": 4, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
 {"tstamp": 1722282495.2203, "task_type": "clustering", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "347d0736c1ba4f68a5d6d5f67b571e06", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": ["Mars", "Soccer", "Copper", "Democracy", "Jupiter", "Basketball", "Silver", "Monarchy", "Saturn", "Tennis", "Gold", "Oligarchy"], "0_ncluster": 4, "0_output": "", "0_ndim": "2D (press for 3D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "7d63eb033b0c457287d4d9ca30ef35f4", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": ["Mars", "Soccer", "Copper", "Democracy", "Jupiter", "Basketball", "Silver", "Monarchy", "Saturn", "Tennis", "Gold", "Oligarchy"], "1_ncluster": 4, "1_output": "", "1_ndim": "2D (press for 3D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}

data/clustering_battle-8d38bb89-1e1a-471a-8b9e-35c1f784690e.jsonl

@@ -5,8 +5,6 @@
 {"tstamp": 1722377407.9489, "task_type": "clustering", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "b47a798fe77b470ab1c1c822619287eb", "0_model_name": "embed-english-v3.0", "0_prompt": ["blonde", "brunette", "gray", "auburn", "white", "redhead", "black", "buffet", "fast casual", "cafe", "sushi bar", "fine dining", "steakhouse"], "0_ncluster": 2, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "cf28dc4c48b448ffa18fdf582654c815", "1_model_name": "intfloat/multilingual-e5-large-instruct", "1_prompt": ["blonde", "brunette", "gray", "auburn", "white", "redhead", "black", "buffet", "fast casual", "cafe", "sushi bar", "fine dining", "steakhouse"], "1_ncluster": 2, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
 {"tstamp": 1722377421.3814, "task_type": "clustering", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "b5fa73c33ab845f69a839a9c35faee7f", "0_model_name": "voyage-multilingual-2", "0_prompt": ["fall", "summer", "winter", "hurricane", "hailstorm", "thunderstorm", "fog", "blizzard", "tornado", "triangle", "pentagon", "hexagon", "saxophone", "bassoon", "semi-arid", "coastal", "cold"], "0_ncluster": 5, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "610e83de059e496fb03afa70d8d905b0", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": ["fall", "summer", "winter", "hurricane", "hailstorm", "thunderstorm", "fog", "blizzard", "tornado", "triangle", "pentagon", "hexagon", "saxophone", "bassoon", "semi-arid", "coastal", "cold"], "1_ncluster": 5, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
 {"tstamp": 1722377446.2025, "task_type": "clustering", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "fb373bc4a5024315b398902a1aba49e9", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": ["muay thai", "kung fu", "rupee", "yen", "bass", "mackerel", "trout"], "0_ncluster": 3, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "b978f54eb46d43958c35fe696b370749", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": ["muay thai", "kung fu", "rupee", "yen", "bass", "mackerel", "trout"], "1_ncluster": 3, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
-{"tstamp": 1722377473.7748, "task_type": "clustering", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "67b841dd8d884ef1ab954462d1bad8fb", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": ["bowler", "beanie", "baseball cap", "fedora", "Twitter", "Snapchat", "Pinterest", "LinkedIn", "Facebook", "Instagram", "TikTok", "K", "C", "D", "B1", "E", "B12", "pu-erh", "green", "black", "rooibos", "oolong"], "0_ncluster": 4, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "69dff80527e54f34bedfa615d412b956", "1_model_name": "text-embedding-3-large", "1_prompt": ["bowler", "beanie", "baseball cap", "fedora", "Twitter", "Snapchat", "Pinterest", "LinkedIn", "Facebook", "Instagram", "TikTok", "K", "C", "D", "B1", "E", "B12", "pu-erh", "green", "black", "rooibos", "oolong"], "1_ncluster": 4, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
-{"tstamp": 1722377490.1182, "task_type": "clustering", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "f9112944819446cebe4767bce9582e33", "0_model_name": "text-embedding-3-large", "0_prompt": ["period", "question mark", "hyphen", "comma", "exclamation point", "Triceratops", "Brachiosaurus", "Pteranodon", "Velociraptor", "Tyrannosaurus", "hoe", "rake"], "0_ncluster": 3, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "568fac9ef0b94b658c1cabf6e0bbb2a8", "1_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "1_prompt": ["period", "question mark", "hyphen", "comma", "exclamation point", "Triceratops", "Brachiosaurus", "Pteranodon", "Velociraptor", "Tyrannosaurus", "hoe", "rake"], "1_ncluster": 3, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
 {"tstamp": 1722377503.3609, "task_type": "clustering", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "40ec871225ac4867a36c829fb567cb66", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": ["Uranus", "Saturn", "Venus", "Earth", "Jupiter", "Neptune", "Mercury", "Mars", "denim", "polyester", "wool", "leather", "silk", "judo", "jiu-jitsu", "muay thai", "karate"], "0_ncluster": 3, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "0cab3fa8cb834f3da653e0d975a34ffd", "1_model_name": "text-embedding-3-large", "1_prompt": ["Uranus", "Saturn", "Venus", "Earth", "Jupiter", "Neptune", "Mercury", "Mars", "denim", "polyester", "wool", "leather", "silk", "judo", "jiu-jitsu", "muay thai", "karate"], "1_ncluster": 3, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
 {"tstamp": 1722377513.9379, "task_type": "clustering", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "71f8f333ef3c4fcd9632589f4a321168", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": ["Cassiopeia", "Orion", "Ursa Major", "Leo", "Taurus", "Scorpius", "Cygnus", "sapphire", "amethyst", "topaz", "emerald", "opal", "Australia", "Europe", "Africa"], "0_ncluster": 3, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "ed5d75165ef749faaed54b6863c1dc20", "1_model_name": "intfloat/multilingual-e5-large-instruct", "1_prompt": ["Cassiopeia", "Orion", "Ursa Major", "Leo", "Taurus", "Scorpius", "Cygnus", "sapphire", "amethyst", "topaz", "emerald", "opal", "Australia", "Europe", "Africa"], "1_ncluster": 3, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
 {"tstamp": 1722377569.7391, "task_type": "clustering", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "b05c7f7456af476eab055dc6fcb42b8d", "0_model_name": "Salesforce/SFR-Embedding-2_R", "0_prompt": ["biology", "geography", "chemistry", "Tyrannosaurus", "Triceratops", "Pteranodon", "Stegosaurus", "Brachiosaurus", "Velociraptor", "Ankylosaurus", "suspension", "beam", "summer", "winter", "fall", "spring", "Jupiter", "Earth"], "0_ncluster": 5, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "7727f8450c0645af96e2e0fc3a9fda79", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": ["biology", "geography", "chemistry", "Tyrannosaurus", "Triceratops", "Pteranodon", "Stegosaurus", "Brachiosaurus", "Velociraptor", "Ankylosaurus", "suspension", "beam", "summer", "winter", "fall", "spring", "Jupiter", "Earth"], "1_ncluster": 5, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}

data/clustering_battle-f62bcad9-19c4-430b-a7ba-aa2bfeba99dc.jsonl

@@ -1,2 +1 @@
-{"tstamp": 1722419237.1568, "task_type": "clustering", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "1e7547b40a7247f798f92e91dba29f04", "0_model_name": "embed-english-v3.0", "0_prompt": ["LHR", "BER", "DUB", "SYD", "JFK", "LTN", "FRA", "SFO", "LTN", "SIN", "IST", "DBX"], "0_ncluster": 1, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "053450d09e7d47c1ba1a39e226cdfbc4", "1_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "1_prompt": ["LHR", "BER", "DUB", "SYD", "JFK", "LTN", "FRA", "SFO", "LTN", "SIN", "IST", "DBX"], "1_ncluster": 1, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
 {"tstamp": 1722419487.8862, "task_type": "clustering", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "346e5cbb1f934cf6911d6ce7bfa3d236", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": ["LHR", "BER", "DUB", "SYD", "JFK", "LTN", "FRA", "SFO", "LTN", "SIN", "IST", "DBX"], "0_ncluster": 3, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "ba8218c6c5ce4b98ae2e8e4e97573696", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_prompt": ["LHR", "BER", "DUB", "SYD", "JFK", "LTN", "FRA", "SFO", "LTN", "SIN", "IST", "DBX"], "1_ncluster": 3, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}

data/clustering_individual-0325b067-7cac-407c-9ffa-76d99f10c9f7.jsonl

@@ -6,5 +6,4 @@
 {"tstamp": 1722263931.8531, "task_type": "clustering", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722263931.6012, "finish": 1722263931.8531, "ip": "", "conv_id": "03ecd85e1a8d4636b1269ba87c1b444a", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": ["Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722263931.8531, "task_type": "clustering", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722263931.6012, "finish": 1722263931.8531, "ip": "", "conv_id": "f41d5abfc81e464f99649a2115bbc6f3", "model_name": "text-embedding-004", "prompt": ["Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722263938.0366, "task_type": "clustering", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722263937.9651, "finish": 1722263938.0366, "ip": "", "conv_id": "d7c52d02fc6b48deb1994e527ca75361", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": ["Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
-{"tstamp": 1722264046.8499, "task_type": "clustering", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722264046.7437, "finish": 1722264046.8499, "ip": "", "conv_id": "8320a5246e234aeeaa0d5ca7c4a9aed1", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": ["Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas", "Mickey Mouse", "Donald Duck", "Charizard"], "ncluster": 4, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722264046.8499, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722264046.7437, "finish": 1722264046.8499, "ip": "", "conv_id": "2c8225553fcc4e4fb2cebb7679a345c8", "model_name": "GritLM/GritLM-7B", "prompt": ["Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas", "Mickey Mouse", "Donald Duck", "Charizard"], "ncluster": 4, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}

data/clustering_individual-115786ef-c6bb-4ff6-b66a-85511fd72c53.jsonl

@@ -8,7 +8,6 @@
 {"tstamp": 1722282019.9861, "task_type": "clustering", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722282019.8814, "finish": 1722282019.9861, "ip": "", "conv_id": "d38c4321a8774856b4c9abff2ce6cad4", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": ["It's official! 1 Bitcoin = $10,000 USD", "Everyone who's trading BTC right now", "Age reversal not only achievable but also possibly imminent: Retro Biosciences", "MicroRNA regrows 90% of lost hair, study finds", "Researchers have found that people who live beyond 105 years tend to have a unique genetic background that makes their bodies more efficient at repairing DNA, according to a new study.", "[D] A Demo from 1993 of 32-year-old Yann LeCun showing off the World's first Convolutional Network for Text Recognition", "Speech-to-speech translation for a real-world unwritten language", "Seeking the Best Embedding Model: Experiences with bge & GritLM?"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722282019.9861, "task_type": "clustering", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722282019.8814, "finish": 1722282019.9861, "ip": "", "conv_id": "65c42b5a4f884b98b026a3ffef82ea54", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": ["It's official! 1 Bitcoin = $10,000 USD", "Everyone who's trading BTC right now", "Age reversal not only achievable but also possibly imminent: Retro Biosciences", "MicroRNA regrows 90% of lost hair, study finds", "Researchers have found that people who live beyond 105 years tend to have a unique genetic background that makes their bodies more efficient at repairing DNA, according to a new study.", "[D] A Demo from 1993 of 32-year-old Yann LeCun showing off the World's first Convolutional Network for Text Recognition", "Speech-to-speech translation for a real-world unwritten language", "Seeking the Best Embedding Model: Experiences with bge & GritLM?"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722282044.0972, "task_type": "clustering", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722282043.4357, "finish": 1722282044.0972, "ip": "", "conv_id": "a5e394365c244068b232bddcafe5199b", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": ["China'\u0080\u0099s statistics chief Wang Baoan detained in graft investigation", "NSFW: Wuhan man arrested by police for live-streaming his midnight lovemaking session", "Planetary Yagya ,Graha Shanti Yagya Bring Out Happiness in Your Life -astrodevam.com", "I am searching a particular type of gel/resin"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
-{"tstamp": 1722282044.0972, "task_type": "clustering", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722282043.4357, "finish": 1722282044.0972, "ip": "", "conv_id": "308dd4a8eab841c7a29837dc131fdfe8", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": ["China'\u0080\u0099s statistics chief Wang Baoan detained in graft investigation", "NSFW: Wuhan man arrested by police for live-streaming his midnight lovemaking session", "Planetary Yagya ,Graha Shanti Yagya Bring Out Happiness in Your Life -astrodevam.com", "I am searching a particular type of gel/resin"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722282073.5833, "task_type": "clustering", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722282073.4876, "finish": 1722282073.5833, "ip": "", "conv_id": "fd2c4be1ef0a412e8bc4bcecb945911a", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722282073.5833, "task_type": "clustering", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722282073.4876, "finish": 1722282073.5833, "ip": "", "conv_id": "d2fbe7711efa4929b9c20f2eec4f562b", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722282080.0079, "task_type": "clustering", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722282079.9087, "finish": 1722282080.0079, "ip": "", "conv_id": "2290c3b15638476492f9bc3e96f1e14f", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}

data/clustering_individual-1fa504af-967a-4ff8-a6ff-4abf5fbdd1c5.jsonl

@@ -3,4 +3,3 @@
 {"tstamp": 1722266433.1051, "task_type": "clustering", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722266432.1713, "finish": 1722266433.1051, "ip": "", "conv_id": "e40f45b38d8d4568a17340ab5c831eda", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": ["Tesla\u2019s Model 3 will boast a 200-mile or greater range, cost $35,000", "How to Buy an Android Car DVD with GPS Navigation?", "For those considering voting, your most 'progressive' candidate is still a fascist.", "She's plotting her revenge guys, help!", "Bandar Bola - Apa Sucker Akan Beli Obligasi Lotus Esprit Submarine Untuk $ 1.000.000?", "Durch Die Nacht Mit... Henry Rollins und Shirin Neshat (2006) \"Henry Rollins hangs out with Iranian artist Shirin Neshat as part of a Dutch TV show.\"", "Black Lives Matter protesters block San Francisco's Bay Bridge", "Ceiling Cat vs. Basement Cat; Arial Image from Syria Shows Biblical Struggle", "\u201cAllure\u201d by any other name: The double standards of rape culture, racism, and gender in pop music", "New Zealand's Transgender Prisoners Fear Double Bunking Will Lead to More Rape", "Homeless return to Sacramento City Hall under political, legal cloud", "Meet Rogan and Bosley, two senior grumpy Persian brothers"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722266433.1051, "task_type": "clustering", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722266432.1713, "finish": 1722266433.1051, "ip": "", "conv_id": "e7b78554b50d469abeebf74e9aef95f0", "model_name": "text-embedding-3-large", "prompt": ["Tesla\u2019s Model 3 will boast a 200-mile or greater range, cost $35,000", "How to Buy an Android Car DVD with GPS Navigation?", "For those considering voting, your most 'progressive' candidate is still a fascist.", "She's plotting her revenge guys, help!", "Bandar Bola - Apa Sucker Akan Beli Obligasi Lotus Esprit Submarine Untuk $ 1.000.000?", "Durch Die Nacht Mit... Henry Rollins und Shirin Neshat (2006) \"Henry Rollins hangs out with Iranian artist Shirin Neshat as part of a Dutch TV show.\"", "Black Lives Matter protesters block San Francisco's Bay Bridge", "Ceiling Cat vs. Basement Cat; Arial Image from Syria Shows Biblical Struggle", "\u201cAllure\u201d by any other name: The double standards of rape culture, racism, and gender in pop music", "New Zealand's Transgender Prisoners Fear Double Bunking Will Lead to More Rape", "Homeless return to Sacramento City Hall under political, legal cloud", "Meet Rogan and Bosley, two senior grumpy Persian brothers"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722266471.2981, "task_type": "clustering", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722266470.8129, "finish": 1722266471.2981, "ip": "", "conv_id": "03d7d22aca0c4ecca15bcf433638a302", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": ["Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas", "Mickey Mouse", "Donald Duck", "Charizard"], "ncluster": 4, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
-{"tstamp": 1722266471.2981, "task_type": "clustering", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722266470.8129, "finish": 1722266471.2981, "ip": "", "conv_id": "1c053f39785540928673118b8d40060b", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": ["Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas", "Mickey Mouse", "Donald Duck", "Charizard"], "ncluster": 4, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}

data/clustering_individual-25bfac43-43c3-4e03-a7e4-e33df32ce74f.jsonl

@@ -1,2 +1 @@
 {"tstamp": 1722570704.7596, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722570697.3439, "finish": 1722570704.7596, "ip": "", "conv_id": "8683a71e8b624246b213378672543da4", "model_name": "GritLM/GritLM-7B", "prompt": ["Pinterest", "Facebook", "Twitter", "TikTok", "Snapchat", "LinkedIn", "Google", "LG", "Xiaomi", "Apple", "OnePlus", "Huawei", "lungs", "kidneys", "liver", "brain", "stomach", "heart", "pancreas"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
-{"tstamp": 1722570704.7596, "task_type": "clustering", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722570697.3439, "finish": 1722570704.7596, "ip": "", "conv_id": "83fb62641c5147098276de351fcefb6a", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": ["Pinterest", "Facebook", "Twitter", "TikTok", "Snapchat", "LinkedIn", "Google", "LG", "Xiaomi", "Apple", "OnePlus", "Huawei", "lungs", "kidneys", "liver", "brain", "stomach", "heart", "pancreas"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}

data/clustering_individual-40ef8ce0-457c-41e8-8b3f-024c4ed67062.jsonl

@@ -5,7 +5,6 @@
 {"tstamp": 1722272681.3354, "task_type": "clustering", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722272680.0512, "finish": 1722272681.3354, "ip": "", "conv_id": "48fc757308974384a3aeab8df4498fb0", "model_name": "text-embedding-004", "prompt": ["Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas", "Mickey Mouse", "Donald Duck", "Charizard"], "ncluster": 4, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722272699.7804, "task_type": "clustering", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722272699.6122, "finish": 1722272699.7804, "ip": "", "conv_id": "27e3e54b3d7940bf88b4701e1d3dffd2", "model_name": "embed-english-v3.0", "prompt": ["Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas", "Mickey Mouse", "Donald Duck", "Charizard"], "ncluster": 4, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722272715.1268, "task_type": "clustering", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722272715.0567, "finish": 1722272715.1268, "ip": "", "conv_id": "b1b58377690d449e991b541dfeb36505", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": ["If someone online buys something off of my Amazon wish list, do they get my full name and address?", "Package \"In Transit\" over a week. No scheduled delivery date, no locations. What's up?", "Can Amazon gift cards replace a debit card?", "Homesick GWS star Cameron McCarthy on road to recovery", "Accidently ordered 2 of an item, how do I only return 1? For free?", "Need help ASAP, someone ordering in my account", "So who's everyone tipping for Round 1?"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
-{"tstamp": 1722272732.504, "task_type": "clustering", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722272732.3458, "finish": 1722272732.504, "ip": "", "conv_id": "aaf53b73902e4107a402db70d25264ca", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": ["If someone online buys something off of my Amazon wish list, do they get my full name and address?", "Package \"In Transit\" over a week. No scheduled delivery date, no locations. What's up?", "Can Amazon gift cards replace a debit card?", "Homesick GWS star Cameron McCarthy on road to recovery", "Accidently ordered 2 of an item, how do I only return 1? For free?", "Need help ASAP, someone ordering in my account", "So who's everyone tipping for Round 1?"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722272732.504, "task_type": "clustering", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722272732.3458, "finish": 1722272732.504, "ip": "", "conv_id": "30be23d062be424caf302edd1c2e22ca", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": ["If someone online buys something off of my Amazon wish list, do they get my full name and address?", "Package \"In Transit\" over a week. No scheduled delivery date, no locations. What's up?", "Can Amazon gift cards replace a debit card?", "Homesick GWS star Cameron McCarthy on road to recovery", "Accidently ordered 2 of an item, how do I only return 1? For free?", "Need help ASAP, someone ordering in my account", "So who's everyone tipping for Round 1?"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722272741.8468, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722272741.6743, "finish": 1722272741.8468, "ip": "", "conv_id": "f0f065cf36e34bd1a099cea4a12a1dc5", "model_name": "GritLM/GritLM-7B", "prompt": ["If someone online buys something off of my Amazon wish list, do they get my full name and address?", "Package \"In Transit\" over a week. No scheduled delivery date, no locations. What's up?", "Can Amazon gift cards replace a debit card?", "Homesick GWS star Cameron McCarthy on road to recovery", "Accidently ordered 2 of an item, how do I only return 1? For free?", "Need help ASAP, someone ordering in my account", "So who's everyone tipping for Round 1?"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722272741.8468, "task_type": "clustering", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722272741.6743, "finish": 1722272741.8468, "ip": "", "conv_id": "211243c37f16458f9da56ee350d9cbaa", "model_name": "BAAI/bge-large-en-v1.5", "prompt": ["If someone online buys something off of my Amazon wish list, do they get my full name and address?", "Package \"In Transit\" over a week. No scheduled delivery date, no locations. What's up?", "Can Amazon gift cards replace a debit card?", "Homesick GWS star Cameron McCarthy on road to recovery", "Accidently ordered 2 of an item, how do I only return 1? For free?", "Need help ASAP, someone ordering in my account", "So who's everyone tipping for Round 1?"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}

data/clustering_individual-5ff75294-3b96-4e61-b91f-99fe468ad573.jsonl

@@ -32,7 +32,6 @@
 {"tstamp": 1722277103.295, "task_type": "clustering", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722277103.2234, "finish": 1722277103.295, "ip": "", "conv_id": "8048f56b44c849d9ad055d7546e18cec", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722277111.7894, "task_type": "clustering", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722277111.7136, "finish": 1722277111.7894, "ip": "", "conv_id": "5921887845dd4858a593949957d80b75", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": ["Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas", "Mickey Mouse", "Donald Duck", "Charizard"], "ncluster": 4, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722277111.7894, "task_type": "clustering", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722277111.7136, "finish": 1722277111.7894, "ip": "", "conv_id": "1edfd3448a4045679dcce57a9b2fc0ae", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": ["Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas", "Mickey Mouse", "Donald Duck", "Charizard"], "ncluster": 4, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
-{"tstamp": 1722277123.4037, "task_type": "clustering", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722277122.6367, "finish": 1722277123.4037, "ip": "", "conv_id": "3531fbc01fe146b4a3f33ece3e084c19", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": ["which airlines fly from boston to washington dc via other cities", "show me the airlines that fly between toronto and denver", "show me round trip first class tickets from new york to miami", "i'd like the lowest fare from denver to pittsburgh", "show me a list of ground transportation at boston airport", "show me boston ground transportation", "of all airlines which airline has the most arrivals in atlanta", "what ground transportation is available in boston", "i would like your rates between atlanta and boston on september third", "which airlines fly between boston and pittsburgh"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722277123.4037, "task_type": "clustering", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722277122.6367, "finish": 1722277123.4037, "ip": "", "conv_id": "9dd9bc6dc31e43ffb9e283c57e012512", "model_name": "text-embedding-004", "prompt": ["which airlines fly from boston to washington dc via other cities", "show me the airlines that fly between toronto and denver", "show me round trip first class tickets from new york to miami", "i'd like the lowest fare from denver to pittsburgh", "show me a list of ground transportation at boston airport", "show me boston ground transportation", "of all airlines which airline has the most arrivals in atlanta", "what ground transportation is available in boston", "i would like your rates between atlanta and boston on september third", "which airlines fly between boston and pittsburgh"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722277139.5959, "task_type": "clustering", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722277138.8294, "finish": 1722277139.5959, "ip": "", "conv_id": "f8c4bf85d67f41de9b8bbfa14f7ebd1b", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": ["Piano", "Electron", "Sushi", "Violin", "Proton", "Pasta", "Trumpet", "Neutron", "Steak", "Clarinet", "Quark", "Salad", "Harp", "Photon", "Soup", "Cello", "Neutrino", "Sandwich"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722277139.5959, "task_type": "clustering", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722277138.8294, "finish": 1722277139.5959, "ip": "", "conv_id": "1d4dc82e83524d199f681f7dd61a25f3", "model_name": "text-embedding-004", "prompt": ["Piano", "Electron", "Sushi", "Violin", "Proton", "Pasta", "Trumpet", "Neutron", "Steak", "Clarinet", "Quark", "Salad", "Harp", "Photon", "Soup", "Cello", "Neutrino", "Sandwich"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
@@ -55,7 +54,6 @@
 {"tstamp": 1722277359.4726, "task_type": "clustering", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722277359.2175, "finish": 1722277359.4726, "ip": "", "conv_id": "5b7e0242a44941139c677d4c8daac524", "model_name": "embed-english-v3.0", "prompt": ["Eagle", "Penguin", "Sparrow", "Ostrich", "Seagull", "Kiwi", "Hawk", "Emu", "Robin", "Chicken", "Dove", "Flamingo", "Owl", "Dodo", "Hummingbird", "Cassowary"], "ncluster": 2, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722277409.5527, "task_type": "clustering", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722277409.1987, "finish": 1722277409.5527, "ip": "", "conv_id": "946bc869b5f34e08b30b11ee46a6ce1a", "model_name": "voyage-multilingual-2", "prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "ncluster": 1, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722277409.5527, "task_type": "clustering", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722277409.1987, "finish": 1722277409.5527, "ip": "", "conv_id": "95c76c91289141e9962fd6bf145eaa9a", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "ncluster": 1, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
-{"tstamp": 1722277417.2246, "task_type": "clustering", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722277416.8376, "finish": 1722277417.2246, "ip": "", "conv_id": "5459804ea04147b2ab86aa6b5f4eb229", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "ncluster": 4, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722277417.2246, "task_type": "clustering", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722277416.8376, "finish": 1722277417.2246, "ip": "", "conv_id": "c31279469df84748acc2819f595c1a05", "model_name": "voyage-multilingual-2", "prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "ncluster": 4, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722277439.4286, "task_type": "clustering", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722277438.7525, "finish": 1722277439.4286, "ip": "", "conv_id": "0c3b0361b0f14423ae0b8081f781a2ec", "model_name": "BAAI/bge-large-en-v1.5", "prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "ncluster": 1, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722277439.4286, "task_type": "clustering", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722277438.7525, "finish": 1722277439.4286, "ip": "", "conv_id": "6522beccc4b44fffb049889a46d4ecd8", "model_name": "text-embedding-004", "prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "ncluster": 1, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
@@ -67,7 +65,6 @@
 {"tstamp": 1722277513.1567, "task_type": "clustering", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722277513.0677, "finish": 1722277513.1567, "ip": "", "conv_id": "4d7cdf865a6141ff9a0097f1840b007c", "model_name": "BAAI/bge-large-en-v1.5", "prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "ncluster": 4, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722277558.3334, "task_type": "clustering", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722277558.2458, "finish": 1722277558.3334, "ip": "", "conv_id": "2b2185253ff94ae49c4e852521ffe4b3", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "ncluster": 4, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722277574.093, "task_type": "clustering", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722277573.9781, "finish": 1722277574.093, "ip": "", "conv_id": "109315a8077f467fbbe89301b167d988", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "ncluster": 4, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
-{"tstamp": 1722277574.093, "task_type": "clustering", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722277573.9781, "finish": 1722277574.093, "ip": "", "conv_id": "8b2a63efc1364029bdee4cabc7bcc79e", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "ncluster": 4, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722277594.2576, "task_type": "clustering", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722277594.176, "finish": 1722277594.2576, "ip": "", "conv_id": "b5be4399c1d3491487992d132ed1ba24", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "ncluster": 4, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722277594.2576, "task_type": "clustering", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722277594.176, "finish": 1722277594.2576, "ip": "", "conv_id": "66410239cb954ef282b1131aaf0aba1b", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "ncluster": 4, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722277616.3261, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722277615.6056, "finish": 1722277616.3261, "ip": "", "conv_id": "1ec040e83c874eb8904ba54ab7663eb1", "model_name": "GritLM/GritLM-7B", "prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "ncluster": 4, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
@@ -75,7 +72,6 @@
 {"tstamp": 1722277643.6176, "task_type": "clustering", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722277643.5225, "finish": 1722277643.6176, "ip": "", "conv_id": "c84d74b682b94723becd3460706e99de", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "ncluster": 4, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722277643.6176, "task_type": "clustering", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722277643.5225, "finish": 1722277643.6176, "ip": "", "conv_id": "1c575591f9084ce0ae92efaef9e08dc5", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "ncluster": 4, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722277660.6456, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722277660.5506, "finish": 1722277660.6456, "ip": "", "conv_id": "88864c80f832487898121865b1636a91", "model_name": "GritLM/GritLM-7B", "prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "ncluster": 4, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
-{"tstamp": 1722277660.6456, "task_type": "clustering", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722277660.5506, "finish": 1722277660.6456, "ip": "", "conv_id": "fb5d34913809493d932b99c9ef9c6ec8", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "ncluster": 4, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722277679.4958, "task_type": "clustering", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722277678.83, "finish": 1722277679.4958, "ip": "", "conv_id": "f18d9dc29d89449e920ee22df252eba0", "model_name": "text-embedding-004", "prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "ncluster": 4, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722277679.4958, "task_type": "clustering", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722277678.83, "finish": 1722277679.4958, "ip": "", "conv_id": "6f854fced7744423a11bfab80d2d5dbf", "model_name": "BAAI/bge-large-en-v1.5", "prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "ncluster": 4, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722277698.0114, "task_type": "clustering", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722277697.9225, "finish": 1722277698.0114, "ip": "", "conv_id": "0b12d1048e4b439dbf8a9199bf4cb161", "model_name": "BAAI/bge-large-en-v1.5", "prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "ncluster": 4, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
@@ -102,9 +98,7 @@
 {"tstamp": 1722277930.5882, "task_type": "clustering", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722277930.4983, "finish": 1722277930.5882, "ip": "", "conv_id": "41b9a03eda0d40bbb9faee0c9ff4a408", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": ["Mars", "Soccer", "Copper", "Democracy", "Jupiter", "Basketball", "Silver", "Monarchy", "Saturn", "Tennis", "Gold", "Oligarchy"], "ncluster": 4, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722277954.5866, "task_type": "clustering", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722277954.5005, "finish": 1722277954.5866, "ip": "", "conv_id": "5f0d7b4f509f4da98db2b3e484edc498", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": ["Mars", "Soccer", "Copper", "Democracy", "Jupiter", "Basketball", "Silver", "Monarchy", "Saturn", "Tennis", "Gold", "Oligarchy"], "ncluster": 4, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722277954.5866, "task_type": "clustering", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722277954.5005, "finish": 1722277954.5866, "ip": "", "conv_id": "2258ba02393e44cda398c929bde09289", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": ["Mars", "Soccer", "Copper", "Democracy", "Jupiter", "Basketball", "Silver", "Monarchy", "Saturn", "Tennis", "Gold", "Oligarchy"], "ncluster": 4, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
-{"tstamp": 1722278258.4041, "task_type": "clustering", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722278257.759, "finish": 1722278258.4041, "ip": "", "conv_id": "86bae053a0184844a9c3451a4fde105c", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": ["Mars", "Soccer", "Copper", "Democracy", "Jupiter", "Basketball", "Silver", "Monarchy", "Saturn", "Tennis", "Gold", "Oligarchy"], "ncluster": 4, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722278258.4041, "task_type": "clustering", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722278257.759, "finish": 1722278258.4041, "ip": "", "conv_id": "2024bf4252a34e8b8149a149f71e6ce7", "model_name": "text-embedding-004", "prompt": ["Mars", "Soccer", "Copper", "Democracy", "Jupiter", "Basketball", "Silver", "Monarchy", "Saturn", "Tennis", "Gold", "Oligarchy"], "ncluster": 4, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
-{"tstamp": 1722278262.2478, "task_type": "clustering", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722278261.3298, "finish": 1722278262.2478, "ip": "", "conv_id": "86bae053a0184844a9c3451a4fde105c", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": ["Mars", "Soccer", "Copper", "Democracy", "Jupiter", "Basketball", "Silver", "Monarchy", "Saturn", "Tennis", "Gold", "Oligarchy"], "ncluster": 4, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722278262.2478, "task_type": "clustering", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722278261.3298, "finish": 1722278262.2478, "ip": "", "conv_id": "2024bf4252a34e8b8149a149f71e6ce7", "model_name": "text-embedding-004", "prompt": ["Mars", "Soccer", "Copper", "Democracy", "Jupiter", "Basketball", "Silver", "Monarchy", "Saturn", "Tennis", "Gold", "Oligarchy"], "ncluster": 4, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722278285.3852, "task_type": "clustering", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722278285.0156, "finish": 1722278285.3852, "ip": "", "conv_id": "ddd2aca4d50c4ac199a2a7838b4c2388", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": ["Mars", "Soccer", "Copper", "Democracy", "Jupiter", "Basketball", "Silver", "Monarchy", "Saturn", "Tennis", "Gold", "Oligarchy"], "ncluster": 4, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722278285.3852, "task_type": "clustering", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722278285.0156, "finish": 1722278285.3852, "ip": "", "conv_id": "7a19184923fc4cd49e696614c04820b1", "model_name": "voyage-multilingual-2", "prompt": ["Mars", "Soccer", "Copper", "Democracy", "Jupiter", "Basketball", "Silver", "Monarchy", "Saturn", "Tennis", "Gold", "Oligarchy"], "ncluster": 4, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}

data/clustering_individual-6da8b2cf-9395-4671-ad9c-18b0374353dc.jsonl

@@ -1,7 +1,5 @@
-{"tstamp": 1722349107.6275, "task_type": "clustering", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722349094.4021, "finish": 1722349107.6275, "ip": "", "conv_id": "71fb84eb6d184d7696d7580d67aca12e", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": ["espresso", "macchiato", "latte", "cappuccino", "mocha", "Beagle", "Golden Retriever", "Incan", "Mayan", "Chinese", "Egyptian", "Mesopotamian", "Roman", "Greek"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722349107.6275, "task_type": "clustering", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722349094.4021, "finish": 1722349107.6275, "ip": "", "conv_id": "b004b194eec141559d16af96f8bf5b4c", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": ["espresso", "macchiato", "latte", "cappuccino", "mocha", "Beagle", "Golden Retriever", "Incan", "Mayan", "Chinese", "Egyptian", "Mesopotamian", "Roman", "Greek"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722349123.0737, "task_type": "clustering", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722349122.1219, "finish": 1722349123.0737, "ip": "", "conv_id": "f35ac634a0a64ee992c7c4f5f7529f35", "model_name": "text-embedding-004", "prompt": ["claustrophobia", "nyctophobia", "Egyptian", "Norse", "Greek", "Roman", "Chinese", "Celtic", "JavaScript", "Java", "Ruby", "C++", "Swift", "Go"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
-{"tstamp": 1722349123.0737, "task_type": "clustering", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722349122.1219, "finish": 1722349123.0737, "ip": "", "conv_id": "36ac0867440f40bcbe842096ad5524e8", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": ["claustrophobia", "nyctophobia", "Egyptian", "Norse", "Greek", "Roman", "Chinese", "Celtic", "JavaScript", "Java", "Ruby", "C++", "Swift", "Go"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722349129.359, "task_type": "clustering", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722349128.1893, "finish": 1722349129.359, "ip": "", "conv_id": "38fa4543d8af44f8b2ab25266a87b17f", "model_name": "BAAI/bge-large-en-v1.5", "prompt": ["acrylic", "polypropylene", "PVC", "polyethylene", "polystyrene", "nylon", "boots", "high heels", "flats", "Bulldog", "Golden Retriever", "Beagle", "German Shepherd", "Labrador", "motor", "Purkinje", "romance", "mystery", "fantasy", "historical fiction", "science fiction", "thriller", "horror"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722349129.359, "task_type": "clustering", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722349128.1893, "finish": 1722349129.359, "ip": "", "conv_id": "42ab109889cc4445bdb08e6395c8566d", "model_name": "text-embedding-3-large", "prompt": ["acrylic", "polypropylene", "PVC", "polyethylene", "polystyrene", "nylon", "boots", "high heels", "flats", "Bulldog", "Golden Retriever", "Beagle", "German Shepherd", "Labrador", "motor", "Purkinje", "romance", "mystery", "fantasy", "historical fiction", "science fiction", "thriller", "horror"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722349169.5485, "task_type": "clustering", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722349169.4426, "finish": 1722349169.5485, "ip": "", "conv_id": "8046b20875784ed0bf981492978f17c2", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": ["muay thai", "judo", "taekwondo", "Samsung", "Apple", "LG", "Google", "Huawei", "purple", "red", "yellow", "green", "Firefox", "Edge", "Chrome", "interneuron", "sensory", "pyramidal", "Purkinje"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}

data/clustering_individual-79029e82-3905-4a19-8fd7-0e6319f51acd.jsonl

@@ -6,7 +6,6 @@
 {"tstamp": 1722282363.4669, "task_type": "clustering", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722282358.0763, "finish": 1722282363.4669, "ip": "", "conv_id": "92f1ddd406b34b1c9e9415d72da9f316", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": ["Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas", "Mickey Mouse", "Donald Duck", "Charizard"], "ncluster": 4, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722282363.4669, "task_type": "clustering", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722282358.0763, "finish": 1722282363.4669, "ip": "", "conv_id": "10957173cee142dbba2922f1af8b5a3b", "model_name": "embed-english-v3.0", "prompt": ["Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas", "Mickey Mouse", "Donald Duck", "Charizard"], "ncluster": 4, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722282376.6844, "task_type": "clustering", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722282376.5111, "finish": 1722282376.6844, "ip": "", "conv_id": "d87bc59e4487439d8de7408701843f84", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": ["Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas", "Mickey Mouse", "Donald Duck", "Charizard"], "ncluster": 4, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
-{"tstamp": 1722282376.6844, "task_type": "clustering", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722282376.5111, "finish": 1722282376.6844, "ip": "", "conv_id": "ef0d30af037f4c71b2fb04cc8a708fee", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": ["Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas", "Mickey Mouse", "Donald Duck", "Charizard"], "ncluster": 4, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722282419.867, "task_type": "clustering", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722282419.8014, "finish": 1722282419.867, "ip": "", "conv_id": "fe5d44f1bdcb4ed49c5552d34ffee09a", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": ["Mars", "Soccer", "Copper", "Democracy", "Jupiter", "Basketball", "Silver", "Monarchy", "Saturn", "Tennis", "Gold", "Oligarchy"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722282419.867, "task_type": "clustering", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722282419.8014, "finish": 1722282419.867, "ip": "", "conv_id": "d4eb31b878b14be5ac7c78909087f635", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": ["Mars", "Soccer", "Copper", "Democracy", "Jupiter", "Basketball", "Silver", "Monarchy", "Saturn", "Tennis", "Gold", "Oligarchy"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722282437.6078, "task_type": "clustering", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722282437.4271, "finish": 1722282437.6078, "ip": "", "conv_id": "8a30321ef5a64070a8878f6a2fad4e81", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": ["Mars", "Soccer", "Copper", "Democracy", "Jupiter", "Basketball", "Silver", "Monarchy", "Saturn", "Tennis", "Gold", "Oligarchy"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}

data/clustering_individual-8d38bb89-1e1a-471a-8b9e-35c1f784690e.jsonl

@@ -58,10 +58,8 @@
 {"tstamp": 1722377427.8209, "task_type": "clustering", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722377427.0944, "finish": 1722377427.8209, "ip": "", "conv_id": "a04e91f7f0654213824ab6d43ece5ae4", "model_name": "text-embedding-3-large", "prompt": ["bistro", "sushi bar", "cafe", "fine dining", "koala", "giraffe"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722377438.5758, "task_type": "clustering", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722377438.4864, "finish": 1722377438.5758, "ip": "", "conv_id": "fb373bc4a5024315b398902a1aba49e9", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": ["muay thai", "kung fu", "rupee", "yen", "bass", "mackerel", "trout"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722377438.5758, "task_type": "clustering", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722377438.4864, "finish": 1722377438.5758, "ip": "", "conv_id": "b978f54eb46d43958c35fe696b370749", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": ["muay thai", "kung fu", "rupee", "yen", "bass", "mackerel", "trout"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
-{"tstamp": 1722377462.6459, "task_type": "clustering", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722377461.7337, "finish": 1722377462.6459, "ip": "", "conv_id": "67b841dd8d884ef1ab954462d1bad8fb", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": ["bowler", "beanie", "baseball cap", "fedora", "Twitter", "Snapchat", "Pinterest", "LinkedIn", "Facebook", "Instagram", "TikTok", "K", "C", "D", "B1", "E", "B12", "pu-erh", "green", "black", "rooibos", "oolong"], "ncluster": 4, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722377462.6459, "task_type": "clustering", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722377461.7337, "finish": 1722377462.6459, "ip": "", "conv_id": "69dff80527e54f34bedfa615d412b956", "model_name": "text-embedding-3-large", "prompt": ["bowler", "beanie", "baseball cap", "fedora", "Twitter", "Snapchat", "Pinterest", "LinkedIn", "Facebook", "Instagram", "TikTok", "K", "C", "D", "B1", "E", "B12", "pu-erh", "green", "black", "rooibos", "oolong"], "ncluster": 4, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722377481.2506, "task_type": "clustering", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722377480.2376, "finish": 1722377481.2506, "ip": "", "conv_id": "f9112944819446cebe4767bce9582e33", "model_name": "text-embedding-3-large", "prompt": ["period", "question mark", "hyphen", "comma", "exclamation point", "Triceratops", "Brachiosaurus", "Pteranodon", "Velociraptor", "Tyrannosaurus", "hoe", "rake"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
-{"tstamp": 1722377481.2506, "task_type": "clustering", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722377480.2376, "finish": 1722377481.2506, "ip": "", "conv_id": "568fac9ef0b94b658c1cabf6e0bbb2a8", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": ["period", "question mark", "hyphen", "comma", "exclamation point", "Triceratops", "Brachiosaurus", "Pteranodon", "Velociraptor", "Tyrannosaurus", "hoe", "rake"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722377498.2788, "task_type": "clustering", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722377497.3889, "finish": 1722377498.2788, "ip": "", "conv_id": "40ec871225ac4867a36c829fb567cb66", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": ["Uranus", "Saturn", "Venus", "Earth", "Jupiter", "Neptune", "Mercury", "Mars", "denim", "polyester", "wool", "leather", "silk", "judo", "jiu-jitsu", "muay thai", "karate"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722377498.2788, "task_type": "clustering", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722377497.3889, "finish": 1722377498.2788, "ip": "", "conv_id": "0cab3fa8cb834f3da653e0d975a34ffd", "model_name": "text-embedding-3-large", "prompt": ["Uranus", "Saturn", "Venus", "Earth", "Jupiter", "Neptune", "Mercury", "Mars", "denim", "polyester", "wool", "leather", "silk", "judo", "jiu-jitsu", "muay thai", "karate"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722377508.9608, "task_type": "clustering", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722377508.8712, "finish": 1722377508.9608, "ip": "", "conv_id": "71f8f333ef3c4fcd9632589f4a321168", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": ["Cassiopeia", "Orion", "Ursa Major", "Leo", "Taurus", "Scorpius", "Cygnus", "sapphire", "amethyst", "topaz", "emerald", "opal", "Australia", "Europe", "Africa"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}

data/clustering_individual-b9229914-47bc-4da8-a21b-89329fff8207.jsonl

@@ -1,5 +1,4 @@
 {"tstamp": 1722305009.6263, "task_type": "clustering", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722305004.9177, "finish": 1722305009.6263, "ip": "", "conv_id": "dcf280bdf2f5420281638d2dd9e11974", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": ["diamond", "amethyst", "ruby", "sapphire", "opal", "cappuccino", "mocha", "latte", "oboe", "trumpet", "flute", "saxophone", "clarinet", "trombone", "bassoon", "parabolic", "plane", "concave", "black", "white", "green"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
-{"tstamp": 1722305009.6263, "task_type": "clustering", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722305004.9177, "finish": 1722305009.6263, "ip": "", "conv_id": "7d7bb506678345b39e10ef230d7bf4f7", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": ["diamond", "amethyst", "ruby", "sapphire", "opal", "cappuccino", "mocha", "latte", "oboe", "trumpet", "flute", "saxophone", "clarinet", "trombone", "bassoon", "parabolic", "plane", "concave", "black", "white", "green"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722305185.1488, "task_type": "clustering", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722305185.0712, "finish": 1722305185.1488, "ip": "", "conv_id": "1ffb428de2744076962b8acf8ec4df89", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": ["pyramidal", "motor", "Purkinje", "interneuron", "O", "B", "composite", "cinder cone", "flashlight", "sleeping bag", "whiskey", "tequila"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722305185.1488, "task_type": "clustering", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722305185.0712, "finish": 1722305185.1488, "ip": "", "conv_id": "99f802e985564f459a4c8f1d35672366", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": ["pyramidal", "motor", "Purkinje", "interneuron", "O", "B", "composite", "cinder cone", "flashlight", "sleeping bag", "whiskey", "tequila"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722305188.8759, "task_type": "clustering", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722305188.8027, "finish": 1722305188.8759, "ip": "", "conv_id": "1ffb428de2744076962b8acf8ec4df89", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": ["pyramidal", "motor", "Purkinje", "interneuron", "O", "B", "composite", "cinder cone", "flashlight", "sleeping bag", "whiskey", "tequila", "B12", "K", "B1", "saut\u00e9ing", "grilling", "baking", "frying", "steaming"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}

data/clustering_individual-cc11c9ee-fc93-4cd6-a4b5-246a7f21038c.jsonl

@@ -6,7 +6,6 @@
 {"tstamp": 1722312058.4588, "task_type": "clustering", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722312058.3715, "finish": 1722312058.4588, "ip": "", "conv_id": "8b4846ae81404f1789b93209af878988", "model_name": "BAAI/bge-large-en-v1.5", "prompt": ["chamomile", "black", "white", "rooibos", "rupee", "yuan", "yen", "franc", "polystyrene", "nylon", "acrylic", "polyethylene", "polypropylene", "PVC", "Indian", "Southern", "Arctic"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722312069.4941, "task_type": "clustering", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722312064.7124, "finish": 1722312069.4941, "ip": "", "conv_id": "38ec8f2c9b4e42969d5da586e1d6b4c6", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": ["molar", "premolar", "canine", "wisdom tooth", "incisor", "temperate", "boreal", "mangrove", "cloud", "buffet", "cafe", "bistro", "fast casual", "horror", "drama", "thriller", "alliteration", "irony", "metaphor", "onomatopoeia", "personification"], "ncluster": 5, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722312069.4941, "task_type": "clustering", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722312064.7124, "finish": 1722312069.4941, "ip": "", "conv_id": "35c637773f2e4f52933968a185ac9b34", "model_name": "embed-english-v3.0", "prompt": ["molar", "premolar", "canine", "wisdom tooth", "incisor", "temperate", "boreal", "mangrove", "cloud", "buffet", "cafe", "bistro", "fast casual", "horror", "drama", "thriller", "alliteration", "irony", "metaphor", "onomatopoeia", "personification"], "ncluster": 5, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
-{"tstamp": 1722323583.0147, "task_type": "clustering", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722323582.9291, "finish": 1722323583.0147, "ip": "", "conv_id": "5f0612277df64e90a86422b9377b4b5b", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": ["dome", "block", "fold", "plateau", "volcanic", "tuna", "halibut", "bass"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722323583.0147, "task_type": "clustering", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722323582.9291, "finish": 1722323583.0147, "ip": "", "conv_id": "5d5df83eebd04705b0eba0b90b6c2840", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": ["dome", "block", "fold", "plateau", "volcanic", "tuna", "halibut", "bass"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722332262.454, "task_type": "clustering", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722332262.3737, "finish": 1722332262.454, "ip": "", "conv_id": "376e71040faa4bf5b22698108934a486", "model_name": "BAAI/bge-large-en-v1.5", "prompt": ["toric", "progressive", "octagon", "rectangle", "linen", "polyester", "wool", "silk", "cotton", "leather", "nitrogen", "calcium", "sodium", "iron", "oxygen", "power supply", "CPU", "hard drive", "GPU", "RAM"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722354917.2437, "task_type": "clustering", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722354917.1731, "finish": 1722354917.2437, "ip": "", "conv_id": "1db398f712f84d478f31a6cd2a450750", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": ["North America", "Asia", "linguine", "spaghetti", "lasagna", "fettuccine"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}

data/clustering_individual-dd51f1d2-bf9d-4e42-9a72-5b30dd2bd338.jsonl

@@ -1,6 +1,3 @@
 {"tstamp": 1722356246.5718, "task_type": "clustering", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722356237.5784, "finish": 1722356246.5718, "ip": "", "conv_id": "9675a7eccb644dedb1a6cf986ea21b4e", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": ["PlayStation", "Atari", "Lighthouse of Alexandria", "Statue of Zeus", "Pyramids of Giza", "Hanging Gardens of Babylon", "Temple of Artemis"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
-{"tstamp": 1722356246.5718, "task_type": "clustering", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722356237.5784, "finish": 1722356246.5718, "ip": "", "conv_id": "fb2c4e1f6d514446aefa67cdf1ea33b0", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": ["PlayStation", "Atari", "Lighthouse of Alexandria", "Statue of Zeus", "Pyramids of Giza", "Hanging Gardens of Babylon", "Temple of Artemis"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
-{"tstamp": 1722356306.9393, "task_type": "clustering", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722356306.0229, "finish": 1722356306.9393, "ip": "", "conv_id": "345fa88283054596b466283729f1afee", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": ["linen", "polyester", "cotton", "silk", "wool", "boreal", "mangrove", "cloud", "silt", "peat", "clay", "chalk", "red", "pink", "purple", "catamaran", "yacht", "kayak", "cruise ship", "canoe"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722356306.9393, "task_type": "clustering", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722356306.0229, "finish": 1722356306.9393, "ip": "", "conv_id": "c89bb5011407429998db2c47975ba7fe", "model_name": "embed-english-v3.0", "prompt": ["linen", "polyester", "cotton", "silk", "wool", "boreal", "mangrove", "cloud", "silt", "peat", "clay", "chalk", "red", "pink", "purple", "catamaran", "yacht", "kayak", "cruise ship", "canoe"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
-{"tstamp": 1722356310.4552, "task_type": "clustering", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722356310.1458, "finish": 1722356310.4552, "ip": "", "conv_id": "345fa88283054596b466283729f1afee", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": ["linen", "polyester", "cotton", "silk", "wool", "boreal", "mangrove", "cloud", "silt", "peat", "clay", "chalk", "red", "pink", "purple", "catamaran", "yacht", "kayak", "cruise ship", "canoe", "linguine", "fettuccine", "lasagna", "penne", "fusilli", "spaghetti", "ballad", "free verse", "haiku", "epic", "sonnet", "ode", "whiskey", "tequila"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722356310.4552, "task_type": "clustering", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722356310.1458, "finish": 1722356310.4552, "ip": "", "conv_id": "c89bb5011407429998db2c47975ba7fe", "model_name": "embed-english-v3.0", "prompt": ["linen", "polyester", "cotton", "silk", "wool", "boreal", "mangrove", "cloud", "silt", "peat", "clay", "chalk", "red", "pink", "purple", "catamaran", "yacht", "kayak", "cruise ship", "canoe", "linguine", "fettuccine", "lasagna", "penne", "fusilli", "spaghetti", "ballad", "free verse", "haiku", "epic", "sonnet", "ode", "whiskey", "tequila"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}

data/clustering_individual-f62bcad9-19c4-430b-a7ba-aa2bfeba99dc.jsonl

@@ -2,11 +2,8 @@
 {"tstamp": 1722404039.6586, "task_type": "clustering", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722404030.4965, "finish": 1722404039.6586, "ip": "", "conv_id": "8920ba375d3d4b5b8da667b39fb2aee1", "model_name": "text-embedding-004", "prompt": ["canine", "wisdom tooth", "cricket", "tennis", "sneakers", "high heels", "loafers", "sandals", "boots", "hydroelectric", "biomass", "quill", "crayon", "fountain pen", "pencil", "chalk"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722406142.1568, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722406142.1303, "finish": 1722406142.1568, "ip": "", "conv_id": "65e94e06e60049a0b93f76e75f5f3795", "model_name": "GritLM/GritLM-7B", "prompt": ["T\u00fcrk\u00e7e biliyor musun "], "ncluster": 1, "output": "", "ndim": "3D", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722418842.482, "task_type": "clustering", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722418842.1963, "finish": 1722418842.482, "ip": "", "conv_id": "c1ac01c2f4384e999c782170c0e08531", "model_name": "embed-english-v3.0", "prompt": ["green", "red", "orange", "yellow", "blue", "purple", "pink", "banana", "mango", "pear", "apple", "kiwi", "hoe", "wheelbarrow", "shovel", "RAM", "power supply", "SSD", "motherboard", "CPU", "hard drive", "AWS", "Azure", "IBM Cloud", "Google Cloud"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
-{"tstamp": 1722418842.482, "task_type": "clustering", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722418842.1963, "finish": 1722418842.482, "ip": "", "conv_id": "66e0f885e0ac467b83d06bb1d5811e61", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": ["green", "red", "orange", "yellow", "blue", "purple", "pink", "banana", "mango", "pear", "apple", "kiwi", "hoe", "wheelbarrow", "shovel", "RAM", "power supply", "SSD", "motherboard", "CPU", "hard drive", "AWS", "Azure", "IBM Cloud", "Google Cloud"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722418849.5116, "task_type": "clustering", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722418849.2741, "finish": 1722418849.5116, "ip": "", "conv_id": "c1ac01c2f4384e999c782170c0e08531", "model_name": "embed-english-v3.0", "prompt": ["green", "red", "orange", "yellow", "blue", "purple", "pink", "banana", "mango", "pear", "apple", "kiwi", "hoe", "wheelbarrow", "shovel", "RAM", "power supply", "SSD", "motherboard", "CPU", "hard drive", "AWS", "Azure", "IBM Cloud", "Google Cloud"], "ncluster": 5, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
-{"tstamp": 1722418849.5116, "task_type": "clustering", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722418849.2741, "finish": 1722418849.5116, "ip": "", "conv_id": "66e0f885e0ac467b83d06bb1d5811e61", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": ["green", "red", "orange", "yellow", "blue", "purple", "pink", "banana", "mango", "pear", "apple", "kiwi", "hoe", "wheelbarrow", "shovel", "RAM", "power supply", "SSD", "motherboard", "CPU", "hard drive", "AWS", "Azure", "IBM Cloud", "Google Cloud"], "ncluster": 5, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722419201.6559, "task_type": "clustering", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722419201.4263, "finish": 1722419201.6559, "ip": "", "conv_id": "1e7547b40a7247f798f92e91dba29f04", "model_name": "embed-english-v3.0", "prompt": ["LHR", "BER", "DUB", "SYD", "JFK", "LTN", "FRA", "SFO", "LTN", "SIN", "IST", "DBX"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
-{"tstamp": 1722419201.6559, "task_type": "clustering", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722419201.4263, "finish": 1722419201.6559, "ip": "", "conv_id": "053450d09e7d47c1ba1a39e226cdfbc4", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": ["LHR", "BER", "DUB", "SYD", "JFK", "LTN", "FRA", "SFO", "LTN", "SIN", "IST", "DBX"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722419250.4205, "task_type": "clustering", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722419250.3309, "finish": 1722419250.4205, "ip": "", "conv_id": "346e5cbb1f934cf6911d6ce7bfa3d236", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": ["LHR", "BER", "DUB", "SYD", "JFK", "LTN", "FRA", "SFO", "LTN", "SIN", "IST", "DBX"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722419250.4205, "task_type": "clustering", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722419250.3309, "finish": 1722419250.4205, "ip": "", "conv_id": "ba8218c6c5ce4b98ae2e8e4e97573696", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": ["LHR", "BER", "DUB", "SYD", "JFK", "LTN", "FRA", "SFO", "LTN", "SIN", "IST", "DBX"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722434299.6602, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722434299.6151, "finish": 1722434299.6602, "ip": "", "conv_id": "938bf910e95f4eb393b20ba88e335007", "model_name": "GritLM/GritLM-7B", "prompt": ["Mexico"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}

data/retrieval_battle-81269da8-6dc7-417a-8e0a-245c4a753482.jsonl

@@ -14,11 +14,8 @@
 {"tstamp": 1722383154.8847, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "c813550e8ae045a8b3d32f00751be689", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "0_corpus": "wikipedia", "1_conv_id": "5f54cc7f0d3e482f9bafa85e281fdac1", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_prompt": "Which test was devised to determine whether robots can think?", "1_output": [["Which test was devised to determine whether robots can think?", "Cognitive test\n\nCognitive tests are assessments of the cognitive capabilities of humans and other animals. Tests administered to humans include various forms of IQ tests; those administered to animals include the mirror test (a test of visual self-awareness) and the T maze test (which tests learning ability). Such testing is used in psychology and psychometrics, as well as other fields studying human and animal intelligence."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722383163.0997, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "5c1461c05577427daa977ecf86528580", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "History of robots\n\nIt features a female humanoid automaton standing by a basin filled with water.When the user pulls the lever, the water drains and the female automaton refills the basin. Furthermore, he created a robotic musical band. According to Mark Rosheim, unlike Greek designs Arab automata worked with dramatic illusion and manipulated the human perception for practical application. The segmental gears described in The Book of Knowledge of Ingenious Mechanical Devices, published by Al-Jazari shortly before his death in 1206, appeared 100 years later in the most advanced European clocks. Al-Jazari also published instructions on the construction of humanoid automata. The first water clocks modeled on Arabic designs were constructed in Europe about 1000 CE, possibly on the basis of the information that was transmitted during Muslim-Christian contact in Sicily and Spain. Among the first recorded European water clocks is that of Gerbert of Aurillac, built in 985 CE. Hero's works on automata were translated into Latin amid the 12th century Renaissance. The early 13th-century artist-engineer Villard de Honnecourt sketched plans for several automata. "]], "0_corpus": "wikipedia", "1_conv_id": "307bbc5403b74fa6b3584ec804a64e43", "1_model_name": "embed-english-v3.0", "1_prompt": "Which test was devised to determine whether robots can think?", "1_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe test was introduced by Turing in his 1950 paper \"Computing Machinery and Intelligence\" while working at the University of Manchester. It opens with the words: \"I propose to consider the question, 'Can machines think? Because \"thinking\" is difficult to define, Turing chooses to \"replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\" Turing describes the new form of the problem in terms of a three-person game called the \"imitation game\", in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: \"Are there imaginable digital computers which would do well in the imitation game?\" This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that \"machines can think\".\nSince Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a \"mind\", \"understanding\", or \"consciousness\", regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.\nHistory"]], "1_corpus": "wikipedia"}
 {"tstamp": 1722383173.9883, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "055cf8ef2477453c9bbc185d2473ad10", "0_model_name": "BM25", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "Artificial intelligence\n\nIn the late teens and early 2020s, AGI companies began to deliver programs that created enormous interest. In 2015, AlphaGo, developed by DeepMind, beat the world champion Go player. The program was taught only the rules of the game and developed strategy by itself. GPT-3 is a large language model that was released in 2020 by OpenAI and is capable of generating high-quality human-like text. These programs, and others, inspired an aggressive AI boom, where large companies began investing billions in AI research. According to AI Impacts, about $50 billion annually was invested in \"AI\" around 2022 in the U.S. alone and about 20% of the new U.S. Computer Science PhD graduates have specialized in \"AI\".\nAbout 800,000 \"AI\"-related U.S. job openings existed in 2022.\nPhilosophy\nDefining artificial intelligence\nAlan Turing wrote in 1950 \"I propose to consider the question 'can machines think'?\" He advised changing the question from whether a machine \"thinks\", to \"whether or not it is possible for machinery to show intelligent behaviour\". He devised the Turing test, which measures the ability of a machine to simulate human conversation. Since we can only observe the behavior of the machine, it does not matter if it is \"actually\" thinking or literally has a \"mind\". Turing notes that we can not determine these things about other people but \"it is usual to have a polite convention that everyone thinks.\""]], "0_corpus": "wikipedia", "1_conv_id": "2038ffbb4252425faf75d979468b058e", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "Which test was devised to determine whether robots can think?", "1_output": [["Which test was devised to determine whether robots can think?", "Progress in artificial intelligence\n\nSub-human Optical character recognition for printed text (nearing par-human for Latin-script typewritten text)\nObject recognition\nVarious robotics tasks that may require advances in robot hardware as well as AI, including:\nStable bipedal locomotion: Bipedal robots can walk, but are less stable than human walkers (as of 2017)\nHumanoid soccer\nSpeech recognition: \"nearly equal to human performance\" (2017)\nExplainability. Current medical systems can diagnose certain medical conditions well, but cannot explain to users why they made the diagnosis.\nMany tests of fluid intelligence (2020)\nBongard visual cognition problems, such as the Bongard-LOGO benchmark (2020)\nVisual Commonsense Reasoning (VCR) benchmark (as of 2020)\nStock market prediction: Financial data collection and processing using Machine Learning algorithms\nAngry Birds video game, as of 2020\nVarious tasks that are difficult to solve without contextual knowledge, including:\nTranslation\nWord-sense disambiguation\nProposed tests of artificial intelligence\nIn his famous Turing test, Alan Turing picked language, the defining feature of human beings, for its basis. The Turing test is now considered too exploitable to be a meaningful benchmark.\nThe Feigenbaum test, proposed by the inventor of expert systems, tests a machine's knowledge and expertise about a specific subject. A paper by Jim Gray of Microsoft in 2003 suggested extending the Turing test to speech understanding, speaking and recognizing objects and behavior."]], "1_corpus": "wikipedia"}
-{"tstamp": 1722383197.0779, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "b46c20808cdb4c899d0052c189fe4f37", "0_model_name": "embed-english-v3.0", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe test was introduced by Turing in his 1950 paper \"Computing Machinery and Intelligence\" while working at the University of Manchester. It opens with the words: \"I propose to consider the question, 'Can machines think? Because \"thinking\" is difficult to define, Turing chooses to \"replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\" Turing describes the new form of the problem in terms of a three-person game called the \"imitation game\", in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: \"Are there imaginable digital computers which would do well in the imitation game?\" This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that \"machines can think\".\nSince Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a \"mind\", \"understanding\", or \"consciousness\", regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.\nHistory"]], "0_corpus": "wikipedia", "1_conv_id": "34a7dc7fdfad44b1966dad58424b74ad", "1_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "1_prompt": "Which test was devised to determine whether robots can think?", "1_output": [["Which test was devised to determine whether robots can think?", "Chinese room\n\nSearle does not disagree that AI research can create machines that are capable of highly intelligent behavior. The Chinese room argument leaves open the possibility that a digital machine could be built that acts more intelligently than a person, but does not have a mind or intentionality in the same way that brains do.\nSearle's \"strong AI hypothesis\" should not be confused with \"strong AI\" as defined by Ray Kurzweil and other futurists, who use the term to describe machine intelligence that rivals or exceeds human intelligence -- that is, artificial general intelligence, human level AI or superintelligence. Kurzweil is referring primarily to the amount of intelligence displayed by the machine, whereas Searle's argument sets no limit on this. Searle argues that a superintelligent machine would not necessarily have a mind and consciousness.\nTuring test\nThe Chinese room implements a version of the Turing test. Alan Turing introduced the test in 1950 to help answer the question \"can machines think?\" In the standard version, a human judge engages in a natural language conversation with a human and a machine designed to generate performance indistinguishable from that of a human being. All participants are separated from one another. If the judge cannot reliably tell the machine from the human, the machine is said to have passed the test."]], "1_corpus": "wikipedia"}
-{"tstamp": 1722383205.5485, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "7f6026b42beb4e1599533d3880f4235c", "0_model_name": "Salesforce/SFR-Embedding-2_R", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "0_corpus": "wikipedia", "1_conv_id": "6cfa70702c3444b3abf7c0c66e34b540", "1_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "1_prompt": "Which test was devised to determine whether robots can think?", "1_output": [["Which test was devised to determine whether robots can think?", "Chinese room\n\nSearle does not disagree that AI research can create machines that are capable of highly intelligent behavior. The Chinese room argument leaves open the possibility that a digital machine could be built that acts more intelligently than a person, but does not have a mind or intentionality in the same way that brains do.\nSearle's \"strong AI hypothesis\" should not be confused with \"strong AI\" as defined by Ray Kurzweil and other futurists, who use the term to describe machine intelligence that rivals or exceeds human intelligence -- that is, artificial general intelligence, human level AI or superintelligence. Kurzweil is referring primarily to the amount of intelligence displayed by the machine, whereas Searle's argument sets no limit on this. Searle argues that a superintelligent machine would not necessarily have a mind and consciousness.\nTuring test\nThe Chinese room implements a version of the Turing test. Alan Turing introduced the test in 1950 to help answer the question \"can machines think?\" In the standard version, a human judge engages in a natural language conversation with a human and a machine designed to generate performance indistinguishable from that of a human being. All participants are separated from one another. If the judge cannot reliably tell the machine from the human, the machine is said to have passed the test."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722383228.7291, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "d0c2bbc549044d0f96953e7e6b6fe99a", "0_model_name": "embed-english-v3.0", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe test was introduced by Turing in his 1950 paper \"Computing Machinery and Intelligence\" while working at the University of Manchester. It opens with the words: \"I propose to consider the question, 'Can machines think? Because \"thinking\" is difficult to define, Turing chooses to \"replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\" Turing describes the new form of the problem in terms of a three-person game called the \"imitation game\", in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: \"Are there imaginable digital computers which would do well in the imitation game?\" This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that \"machines can think\".\nSince Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a \"mind\", \"understanding\", or \"consciousness\", regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.\nHistory"]], "0_corpus": "wikipedia", "1_conv_id": "34cd4fa6ec0f469090cc9e0c62df64b8", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "Which test was devised to determine whether robots can think?", "1_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722383236.588, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "e7ace3aa2a514e1fb18265dbeb385c4c", "0_model_name": "Salesforce/SFR-Embedding-2_R", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "0_corpus": "wikipedia", "1_conv_id": "f4c0338fd74c439cb674aece8875cf8c", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": "Which test was devised to determine whether robots can think?", "1_output": [["Which test was devised to determine whether robots can think?", "History of robots\n\nIt features a female humanoid automaton standing by a basin filled with water.When the user pulls the lever, the water drains and the female automaton refills the basin. Furthermore, he created a robotic musical band. According to Mark Rosheim, unlike Greek designs Arab automata worked with dramatic illusion and manipulated the human perception for practical application. The segmental gears described in The Book of Knowledge of Ingenious Mechanical Devices, published by Al-Jazari shortly before his death in 1206, appeared 100 years later in the most advanced European clocks. Al-Jazari also published instructions on the construction of humanoid automata. The first water clocks modeled on Arabic designs were constructed in Europe about 1000 CE, possibly on the basis of the information that was transmitted during Muslim-Christian contact in Sicily and Spain. Among the first recorded European water clocks is that of Gerbert of Aurillac, built in 985 CE. Hero's works on automata were translated into Latin amid the 12th century Renaissance. The early 13th-century artist-engineer Villard de Honnecourt sketched plans for several automata. "]], "1_corpus": "wikipedia"}
-{"tstamp": 1722383247.863, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "a50b91eb67e4454cb34123819ab2334f", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe test was introduced by Turing in his 1950 paper \"Computing Machinery and Intelligence\" while working at the University of Manchester. It opens with the words: \"I propose to consider the question, 'Can machines think? Because \"thinking\" is difficult to define, Turing chooses to \"replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\" Turing describes the new form of the problem in terms of a three-person game called the \"imitation game\", in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: \"Are there imaginable digital computers which would do well in the imitation game?\" This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that \"machines can think\".\nSince Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a \"mind\", \"understanding\", or \"consciousness\", regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.\nHistory"]], "0_corpus": "wikipedia", "1_conv_id": "dcfc68f4723b4003b74e01c4fc914804", "1_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "1_prompt": "Which test was devised to determine whether robots can think?", "1_output": [["Which test was devised to determine whether robots can think?", "Chinese room\n\nSearle does not disagree that AI research can create machines that are capable of highly intelligent behavior. The Chinese room argument leaves open the possibility that a digital machine could be built that acts more intelligently than a person, but does not have a mind or intentionality in the same way that brains do.\nSearle's \"strong AI hypothesis\" should not be confused with \"strong AI\" as defined by Ray Kurzweil and other futurists, who use the term to describe machine intelligence that rivals or exceeds human intelligence -- that is, artificial general intelligence, human level AI or superintelligence. Kurzweil is referring primarily to the amount of intelligence displayed by the machine, whereas Searle's argument sets no limit on this. Searle argues that a superintelligent machine would not necessarily have a mind and consciousness.\nTuring test\nThe Chinese room implements a version of the Turing test. Alan Turing introduced the test in 1950 to help answer the question \"can machines think?\" In the standard version, a human judge engages in a natural language conversation with a human and a machine designed to generate performance indistinguishable from that of a human being. All participants are separated from one another. If the judge cannot reliably tell the machine from the human, the machine is said to have passed the test."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722383256.029, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "de85d02935494deaacb803069c129482", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe test was introduced by Turing in his 1950 paper \"Computing Machinery and Intelligence\" while working at the University of Manchester. It opens with the words: \"I propose to consider the question, 'Can machines think? Because \"thinking\" is difficult to define, Turing chooses to \"replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\" Turing describes the new form of the problem in terms of a three-person game called the \"imitation game\", in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: \"Are there imaginable digital computers which would do well in the imitation game?\" This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that \"machines can think\".\nSince Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a \"mind\", \"understanding\", or \"consciousness\", regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.\nHistory"]], "0_corpus": "wikipedia", "1_conv_id": "5ee58329351f4a42b51abd844d5c1002", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_prompt": "Which test was devised to determine whether robots can think?", "1_output": [["Which test was devised to determine whether robots can think?", "Cognitive test\n\nCognitive tests are assessments of the cognitive capabilities of humans and other animals. Tests administered to humans include various forms of IQ tests; those administered to animals include the mirror test (a test of visual self-awareness) and the T maze test (which tests learning ability). Such testing is used in psychology and psychometrics, as well as other fields studying human and animal intelligence."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722383265.0457, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "06f5c27330dc45a1b8d79a0da7980082", "0_model_name": "BM25", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "Artificial intelligence\n\nIn the late teens and early 2020s, AGI companies began to deliver programs that created enormous interest. In 2015, AlphaGo, developed by DeepMind, beat the world champion Go player. The program was taught only the rules of the game and developed strategy by itself. GPT-3 is a large language model that was released in 2020 by OpenAI and is capable of generating high-quality human-like text. These programs, and others, inspired an aggressive AI boom, where large companies began investing billions in AI research. According to AI Impacts, about $50 billion annually was invested in \"AI\" around 2022 in the U.S. alone and about 20% of the new U.S. Computer Science PhD graduates have specialized in \"AI\".\nAbout 800,000 \"AI\"-related U.S. job openings existed in 2022.\nPhilosophy\nDefining artificial intelligence\nAlan Turing wrote in 1950 \"I propose to consider the question 'can machines think'?\" He advised changing the question from whether a machine \"thinks\", to \"whether or not it is possible for machinery to show intelligent behaviour\". He devised the Turing test, which measures the ability of a machine to simulate human conversation. Since we can only observe the behavior of the machine, it does not matter if it is \"actually\" thinking or literally has a \"mind\". Turing notes that we can not determine these things about other people but \"it is usual to have a polite convention that everyone thinks.\""]], "0_corpus": "wikipedia", "1_conv_id": "4639b98707f04bac85718774607f15ec", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "Which test was devised to determine whether robots can think?", "1_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722383278.1255, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "11c326bdd41745b7a30a6dab39f56f2d", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "Progress in artificial intelligence\n\nSub-human Optical character recognition for printed text (nearing par-human for Latin-script typewritten text)\nObject recognition\nVarious robotics tasks that may require advances in robot hardware as well as AI, including:\nStable bipedal locomotion: Bipedal robots can walk, but are less stable than human walkers (as of 2017)\nHumanoid soccer\nSpeech recognition: \"nearly equal to human performance\" (2017)\nExplainability. Current medical systems can diagnose certain medical conditions well, but cannot explain to users why they made the diagnosis.\nMany tests of fluid intelligence (2020)\nBongard visual cognition problems, such as the Bongard-LOGO benchmark (2020)\nVisual Commonsense Reasoning (VCR) benchmark (as of 2020)\nStock market prediction: Financial data collection and processing using Machine Learning algorithms\nAngry Birds video game, as of 2020\nVarious tasks that are difficult to solve without contextual knowledge, including:\nTranslation\nWord-sense disambiguation\nProposed tests of artificial intelligence\nIn his famous Turing test, Alan Turing picked language, the defining feature of human beings, for its basis. The Turing test is now considered too exploitable to be a meaningful benchmark.\nThe Feigenbaum test, proposed by the inventor of expert systems, tests a machine's knowledge and expertise about a specific subject. A paper by Jim Gray of Microsoft in 2003 suggested extending the Turing test to speech understanding, speaking and recognizing objects and behavior."]], "0_corpus": "wikipedia", "1_conv_id": "f5fc217bd84943a693a844131ca83e05", "1_model_name": "BM25", "1_prompt": "Which test was devised to determine whether robots can think?", "1_output": [["Which test was devised to determine whether robots can think?", "Artificial intelligence\n\nIn the late teens and early 2020s, AGI companies began to deliver programs that created enormous interest. In 2015, AlphaGo, developed by DeepMind, beat the world champion Go player. The program was taught only the rules of the game and developed strategy by itself. GPT-3 is a large language model that was released in 2020 by OpenAI and is capable of generating high-quality human-like text. These programs, and others, inspired an aggressive AI boom, where large companies began investing billions in AI research. According to AI Impacts, about $50 billion annually was invested in \"AI\" around 2022 in the U.S. alone and about 20% of the new U.S. Computer Science PhD graduates have specialized in \"AI\".\nAbout 800,000 \"AI\"-related U.S. job openings existed in 2022.\nPhilosophy\nDefining artificial intelligence\nAlan Turing wrote in 1950 \"I propose to consider the question 'can machines think'?\" He advised changing the question from whether a machine \"thinks\", to \"whether or not it is possible for machinery to show intelligent behaviour\". He devised the Turing test, which measures the ability of a machine to simulate human conversation. Since we can only observe the behavior of the machine, it does not matter if it is \"actually\" thinking or literally has a \"mind\". Turing notes that we can not determine these things about other people but \"it is usual to have a polite convention that everyone thinks.\""]], "1_corpus": "wikipedia"}
@@ -36,6 +33,5 @@
 {"tstamp": 1722392115.6318, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "415fb556f184488b92d5725a2fa65296", "0_model_name": "voyage-multilingual-2", "0_prompt": "Paper analyzing the impact of misinformation generated by AI on Open-Domain Question Answering systems", "0_output": [["Paper analyzing the impact of misinformation generated by AI on Open-Domain Question Answering systems", "Title: Attacking Open-domain Question Answering by Injecting Misinformation\n\nAbstract: With a rise in false, inaccurate, and misleading information in propaganda, news, and social media, real-world Question Answering (QA) systems face the challenges of synthesizing and reasoning over misinformation-polluted contexts to derive correct answers. This urgency gives rise to the need to make QA systems robust to misinformation, a topic previously unexplored. We study the risk of misinformation to QA models by investigating the sensitivity of open-domain QA models to corpus pollution with misinformation documents. We curate both human-written and model-generated false documents that we inject into the evidence corpus of QA models and assess the impact on the performance of these systems. Experiments show that QA models are vulnerable to even small amounts of evidence contamination brought by misinformation, with large absolute performance drops on all models. Misinformation attack brings more threat when fake documents are produced at scale by neural models or the attacker targets hacking specific questions of interest. To defend against such a threat, we discuss the necessity of building a misinformation-aware QA system that integrates question-answering and misinformation detection in a joint fashion."]], "0_corpus": "arxiv", "1_conv_id": "bf554ef594fa42659fa1dc167287f96d", "1_model_name": "BM25", "1_prompt": "Paper analyzing the impact of misinformation generated by AI on Open-Domain Question Answering systems", "1_output": [["Paper analyzing the impact of misinformation generated by AI on Open-Domain Question Answering systems", "Title: Attacking Open-domain Question Answering by Injecting Misinformation\n\nAbstract: With a rise in false, inaccurate, and misleading information in propaganda, news, and social media, real-world Question Answering (QA) systems face the challenges of synthesizing and reasoning over misinformation-polluted contexts to derive correct answers. This urgency gives rise to the need to make QA systems robust to misinformation, a topic previously unexplored. We study the risk of misinformation to QA models by investigating the sensitivity of open-domain QA models to corpus pollution with misinformation documents. We curate both human-written and model-generated false documents that we inject into the evidence corpus of QA models and assess the impact on the performance of these systems. Experiments show that QA models are vulnerable to even small amounts of evidence contamination brought by misinformation, with large absolute performance drops on all models. Misinformation attack brings more threat when fake documents are produced at scale by neural models or the attacker targets hacking specific questions of interest. To defend against such a threat, we discuss the necessity of building a misinformation-aware QA system that integrates question-answering and misinformation detection in a joint fashion."]], "1_corpus": "arxiv"}
 {"tstamp": 1722392139.5942, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "13b3a212dd6045c5980c9c53a8030a19", "0_model_name": "voyage-multilingual-2", "0_prompt": "suzanne's story orange is the new black", "0_output": [["suzanne's story orange is the new black", "List of Orange Is the New Black characters\n\nWhen Yvonne \"Vee\" Parker enters the prison and forms an African-American gang, Suzanne falls for Vee's charms and maternal influence, being exploited into becoming Vee's \"muscle.\" While zealously loyal to Vee, Suzanne violently beat or threatened any inmates who crossed her, almost acting on command. Later, Vee attempts to coldly trick her into taking the fall for Red's severe beating, as a distraught Suzanne believes she may have done it unconsciously due to her violent history. During the third season, she is encouraged by the new counselor Berdie Rogers to be more creative, causing her to start writing several science fiction erotic stories that become a hit among the women in the prison. Suzanne reveals that she has no sexual experience and is completely na\u00efve in regard to sex, having never actually had a girlfriend before, and that the stories are based on other sources. Eventually, the stories make their way to the staff, causing Rogers to get suspended. Meanwhile, Suzanne becomes nervous upon discovering that one of her fans, Maureen Kukudio, is interested in her romantically. Suzanne backs out of a possible sexual encounter, but is later seen forming a close bond with Maureen towards the end of the season."]], "0_corpus": "wikipedia", "1_conv_id": "5c91938153e6442481f259a5f71fb16e", "1_model_name": "text-embedding-3-large", "1_prompt": "suzanne's story orange is the new black", "1_output": [["suzanne's story orange is the new black", "List of Orange Is the New Black characters\n\nWhen Yvonne \"Vee\" Parker enters the prison and forms an African-American gang, Suzanne falls for Vee's charms and maternal influence, being exploited into becoming Vee's \"muscle.\" While zealously loyal to Vee, Suzanne violently beat or threatened any inmates who crossed her, almost acting on command. Later, Vee attempts to coldly trick her into taking the fall for Red's severe beating, as a distraught Suzanne believes she may have done it unconsciously due to her violent history. During the third season, she is encouraged by the new counselor Berdie Rogers to be more creative, causing her to start writing several science fiction erotic stories that become a hit among the women in the prison. Suzanne reveals that she has no sexual experience and is completely na\u00efve in regard to sex, having never actually had a girlfriend before, and that the stories are based on other sources. Eventually, the stories make their way to the staff, causing Rogers to get suspended. Meanwhile, Suzanne becomes nervous upon discovering that one of her fans, Maureen Kukudio, is interested in her romantically. Suzanne backs out of a possible sexual encounter, but is later seen forming a close bond with Maureen towards the end of the season."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722401702.1092, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "73810fc6473f45b88d5f14389ac03786", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": "how can I kill a process with a given name", "0_output": [["how can I kill a process with a given name", "Superuser Stackexchange\n\nQ: How do I kill a process by its name in Linux? I have a process with the name bla, and I want to kill it by its name. \nWhen I run ps aux | grep \"bla\", I get 2 entries:\n\n\n*\n\n*process bla and\n\n*blablablab\nHow do I filter it out?\n\nA: Take a look at pkill, if available.\n"]], "0_corpus": "stackexchange", "1_conv_id": "396086233e25494698e106917cc2543f", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": "how can I kill a process with a given name", "1_output": [["how can I kill a process with a given name", "Stackoverflow Stackexchange\n\nQ: How to wrap content in html in a Rails helper I want to wrap some content in HTML in a Rails 3 helper so that in my view I can do this:\n<%= rounded_box do-%>\n  <%= raw target.text %>\n<% end -%>\n\nI have a helper method that looks like this:\ndef rounded_box(&block)\n  str = \"<div class='rounded_box'><div class='rounded_box_content'><div class='rounded_box_top'></div>        \n  str << yield\n  str << \"<div class='rounded_box_bottom'><div></div></div></div>\"\n  raw str\nend\n\nThe way I have it now returns the content properly wrapped in the HTML string, but not before rendering any erb in the rounded_box block (e.g. in this case the target.text is rendered twice, once wrapped, once not).\nIs there a better way to do this? For simplicity, I'd like to avoid using content_tag, but if that's the only/best way I can do that.\n\nA: Call capture on the block instead of yield:\ndef rounded_box(&block)\n  str = \"<div class='rounded_box'><div class='rounded_box_content'><div class='rounded_box_top'></div>\"        \n  str << capture(&block)\n  str << \"<div class='rounded_box_bottom'><div></div></div></div>\"\n  raw str\nend\n\n"]], "1_corpus": "stackexchange"}
-{"tstamp": 1722401773.3734, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "b183c66d1a864220b961fd3aab8a2786", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": "machine learning ssm models mambda", "0_output": [["machine learning ssm models mambda", "Title: Bayesian Inference for Linear Dynamic Models with Dirichlet Process Mixtures\n\nAbstract: Using Kalman techniques, it is possible to perform optimal estimation in linear Gaussian state-space models. We address here the case where the noise probability density functions are of unknown functional form. A flexible Bayesian nonparametric noise model based on Dirichlet process mixtures is introduced. Efficient Markov chain Monte Carlo and Sequential Monte Carlo methods are then developed to perform optimal batch and sequential estimation in such contexts. The algorithms are applied to blind deconvolution and change point detection. Experimental results on synthetic and real data demonstrate the efficiency of this approach in various contexts."]], "0_corpus": "arxiv", "1_conv_id": "076e03e824ba49ca95245f716214aec3", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "machine learning ssm models mambda", "1_output": [["machine learning ssm models mambda", "Title: Vision Mamba: A Comprehensive Survey and Taxonomy\n\nAbstract: State Space Model (SSM) is a mathematical model used to describe and analyze the behavior of dynamic systems. This model has witnessed numerous applications in several fields, including control theory, signal processing, economics and machine learning. In the field of deep learning, state space models are used to process sequence data, such as time series analysis, natural language processing (NLP) and video understanding. By mapping sequence data to state space, long-term dependencies in the data can be better captured. In particular, modern SSMs have shown strong representational capabilities in NLP, especially in long sequence modeling, while maintaining linear time complexity. Notably, based on the latest state-space models, Mamba merges time-varying parameters into SSMs and formulates a hardware-aware algorithm for efficient training and inference. Given its impressive efficiency and strong long-range dependency modeling capability, Mamba is expected to become a new AI architecture that may outperform Transformer. Recently, a number of works have attempted to study the potential of Mamba in various fields, such as general vision, multi-modal, medical image analysis and remote sensing image analysis, by extending Mamba from natural language domain to visual domain. To fully understand Mamba in the visual domain, we conduct a comprehensive survey and present a taxonomy study. This survey focuses on Mamba's application to a variety of visual tasks and data types, and discusses its predecessors, recent advances and far-reaching impact on a wide range of domains. Since Mamba is now on an upward trend, please actively notice us if you have new findings, and new progress on Mamba will be included in this survey in a timely manner and updated on the Mamba project at https://github.com/lx6c78/Vision-Mamba-A-Comprehensive-Survey-and-Taxonomy."]], "1_corpus": "arxiv"}
 {"tstamp": 1722403592.4887, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "6f8b359afa074fe9b99960410ca68854", "0_model_name": "embed-english-v3.0", "0_prompt": "I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "0_output": [["I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "Title: MTEB: Massive Text Embedding Benchmark\n\nAbstract: Text embeddings are commonly evaluated on a small set of datasets from a single task not covering their possible applications to other tasks. It is unclear whether state-of-the-art embeddings on semantic textual similarity (STS) can be equally well applied to other tasks like clustering or reranking. This makes progress in the field difficult to track, as various models are constantly being proposed without proper evaluation. To solve this problem, we introduce the Massive Text Embedding Benchmark (MTEB). MTEB spans 8 embedding tasks covering a total of 58 datasets and 112 languages. Through the benchmarking of 33 models on MTEB, we establish the most comprehensive benchmark of text embeddings to date. We find that no particular text embedding method dominates across all tasks. This suggests that the field has yet to converge on a universal text embedding method and scale it up sufficiently to provide state-of-the-art results on all embedding tasks. MTEB comes with open-source code and a public leaderboard at https://github.com/embeddings-benchmark/mteb."]], "0_corpus": "arxiv", "1_conv_id": "ed9faaf063f642cd872d3b354dbe1376", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": "I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "1_output": [["I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "Title: Gauge fixing, BRS invariance and Ward identities for randomly stirred flows\n\nAbstract: The Galilean invariance of the Navier-Stokes equation is shown to be akin to a global gauge symmetry familiar from quantum field theory. This symmetry leads to a multiple counting of infinitely many inertial reference frames in the path integral approach to randomly stirred fluids. This problem is solved by fixing the gauge, i.e., singling out one reference frame. The gauge fixed theory has an underlying Becchi-Rouet-Stora (BRS) symmetry which leads to the Ward identity relating the exact inverse response and vertex functions. This identification of Galilean invariance as a gauge symmetry is explored in detail, for different gauge choices and by performing a rigorous examination of a discretized version of the theory. The Navier-Stokes equation is also invariant under arbitrary rectilinear frame accelerations, known as extended Galilean invariance (EGI). We gauge fix this extended symmetry and derive the generalized Ward identity that follows from the BRS invariance of the gauge-fixed theory. This new Ward identity reduces to the standard one in the limit of zero acceleration. This gauge-fixing approach unambiguously shows that Galilean invariance and EGI constrain only the zero mode of the vertex but none of the higher wavenumber modes."]], "1_corpus": "arxiv"}
 {"tstamp": 1722404064.2696, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "1f792446858649febdcd92f6bf7b0b37", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "Research on combining sentiment analysis and market data for volatility forecasting.", "0_output": [["Research on combining sentiment analysis and market data for volatility forecasting.", "Title: Volatility forecasting using Deep Learning and sentiment analysis\n\nAbstract: Several studies have shown that deep learning models can provide more accurate volatility forecasts than the traditional methods used within this domain. This paper presents a composite model that merges a deep learning approach with sentiment analysis for predicting market volatility. To classify public sentiment, we use a Convolutional Neural Network, which obtained data from Reddit global news headlines. We then describe a composite forecasting model, a Long-Short-Term-Memory Neural Network method, to use historical sentiment and the previous day's volatility to make forecasts. We employed this method on the past volatility of the S&P500 and the major BRICS indices to corroborate its effectiveness. Our results demonstrate that including sentiment can improve Deep Learning volatility forecasting models. However, in contrast to return forecasting, the performance benefits of including sentiment appear for volatility forecasting appears to be market specific."]], "0_corpus": "arxiv", "1_conv_id": "50604820dc7b45a784d897f43d88f88f", "1_model_name": "text-embedding-004", "1_prompt": "Research on combining sentiment analysis and market data for volatility forecasting.", "1_output": [["Research on combining sentiment analysis and market data for volatility forecasting.", "Title: A Sentiment Analysis Approach to the Prediction of Market Volatility\n\nAbstract: Prediction and quantification of future volatility and returns play an important role in financial modelling, both in portfolio optimization and risk management. Natural language processing today allows to process news and social media comments to detect signals of investors' confidence. We have explored the relationship between sentiment extracted from financial news and tweets and FTSE100 movements. We investigated the strength of the correlation between sentiment measures on a given day and market volatility and returns observed the next day. The findings suggest that there is evidence of correlation between sentiment and stock market movements: the sentiment captured from news headlines could be used as a signal to predict market returns; the same does not apply for volatility. Also, in a surprising finding, for the sentiment found in Twitter comments we obtained a correlation coefficient of -0.7, and p-value below 0.05, which indicates a strong negative correlation between positive sentiment captured from the tweets on a given day and the volatility observed the next day. We developed an accurate classifier for the prediction of market volatility in response to the arrival of new information by deploying topic modelling, based on Latent Dirichlet Allocation, to extract feature vectors from a collection of tweets and financial news. The obtained features were used as additional input to the classifier. Thanks to the combination of sentiment and topic modelling our classifier achieved a directional prediction accuracy for volatility of 63%."]], "1_corpus": "arxiv"}

data/retrieval_battle-b9229914-47bc-4da8-a21b-89329fff8207.jsonl

@@ -1,5 +1,4 @@
 {"tstamp": 1722305246.8396, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "6cf9d4db20f8436c8c7f2ccaca29d2fa", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "what is the difference between cluster point and limit point?", "0_output": [["what is the difference between cluster point and limit point?", "Askubuntu Stackexchange\n\nQ: How to enable user sharing per instructions? I have this entry in .xsession-errors log\n\"Nautilus-Share-Message: Called \"net usershare info\" but it failed: 'net usershare' returned \nerror 255: net usershare: cannot open usershare directory /var/lib/samba/usershares.         \nError No such file or directory\nPlease ask your system administrator to enable user sharing.\n\nThis is in relation to my applets having to be reloaded after every boot. Just wondering how I would enable user sharing, and how it affects my applets ??\n\nA: I don't think this effects your applets at all, that's probably a different bug. You could just make the directory with sudo:\nsudo mkdir -p /var/lib/samba/usershares\n\nBut I don't really know what it will do.\n"]], "0_corpus": "stackexchange", "1_conv_id": "38308d8cc72f4a24b8ddc6bafec79a96", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "what is the difference between cluster point and limit point?", "1_output": [["what is the difference between cluster point and limit point?", "Math Stackexchange\n\nQ: Limit and limit points What is the basic difference between limit and limit points, and if a sequence has one unique limit how it can have a lot of limit points \n\nA: A limit point is a generalization of a limit (each limit is a limit point but not vice versa). You can see this in the definition:\nLimit: $a$ is a limit of $(a_n)$, iff in each neighborhood of $a$ are almost all elements of $(a_n)$.\nLimit point: $a$ is a limit of $(a_n)$, iff in each neighborhood of $a$ are infinite elements of $(a_n)$.\nI highlighted the difference between both definitions.\nBecause only infinite elements need to be in any neighborhood of $a$. There can be infinite elements outside this neighborhood which can clustered to another limit point. This is the reason why there might be many limit points for a sequence. If $a$ is a limit only finite elements can be outside any given neighborhood which is not enough to go to another limit.\n"]], "1_corpus": "stackexchange"}
-{"tstamp": 1722306411.5045, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "5993407c26d04c54981a49217a38518d", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": "when did scotland last qualify for world cup", "0_output": [["when did scotland last qualify for world cup", "Scotland national football team\n\nScotland lost just two of their first 43 international matches. It was not until a 2\u20130 home defeat by Ireland in 1903 that Scotland lost a match to a team other than England. This run of success meant that Scotland would have regularly topped the Elo ratings, which were calculated in 1998, between 1876 and 1904. Scotland won the British Home Championship outright on 24 occasions, and shared the title 17 times with at least one other team. A noteworthy victory for Scotland before the Second World War was the 5\u20131 victory over England in 1928, which led to that Scotland side being known as the \"Wembley Wizards\". Scotland played their first match outside the British Isles in 1929, beating Norway 7\u20133 in Bergen. Scotland continued to contest regular friendly matches against European opposition and enjoyed wins against Germany and France before losing to the Austrian \"Wunderteam\" and Italy in 1931.\nScotland, like the other Home Nations, did not enter the three FIFA World Cups held during the 1930s. This was because the four associations had been excluded from FIFA due to a disagreement regarding the status of amateur players. The four associations, including Scotland, returned to the FIFA fold after the Second World War. A match between a United Kingdom team and a \"Rest of the World\" team was played at Hampden Park in 1947 to celebrate this reconciliation.\n1950s: Entering the World Cup"]], "0_corpus": "wikipedia", "1_conv_id": "940948f56e9c4f5297cb17f69935bd8b", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "when did scotland last qualify for world cup", "1_output": [["when did scotland last qualify for world cup", "Scotland at the FIFA World Cup\n\nThe World Cup consists of two parts, the qualification phase and the final phase (officially called the World Cup Finals). The qualification phase, which currently take place over the three years preceding the Finals, is used to determine which teams qualify for the Finals. The current format of the Finals involves 32 teams competing at venues within the host nation (or nations) over a period of about a month. The World Cup Finals is the most widely viewed sporting event in the world, with an estimated 715.1 million people watching the 2006 tournament final.\nHistory\n1930s\nScotland did not compete in the first three World Cup competitions, in 1930, 1934 and 1938. Because of a dispute with FIFA over \"broken-time\" payments to players, the SFA, with the football associations of England, Ireland and Wales, withdrew from FIFA in 1928, and did not rejoin as a permanent member until 1946.\n1950s\nThe readmission of the Scottish Football Association to FIFA in 1946 meant that Scotland were now eligible to enter the 1950 FIFA World Cup. FIFA advised that places would be awarded to the top two teams in the 1949\u201350 British Home Championship, but the SFA announced that Scotland would attend the finals only if Scotland won the competition. Scotland won their first two matches, but a 1\u20130 home defeat by England meant that the Scots finished as runners-up. The SFA stood by this proclamation, despite pleas to the contrary by the Scotland players, supported by England captain Billy Wright and the other England players. Sportswriter Brian Glanville later described the SFA as showing \"baffling insularity and pique\" in their refusal to send a team to the World Cup."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722307573.5271, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "9deb3465ffd3458081570489c3810813", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": "are opened or are open?", "0_output": [["are opened or are open?", "Ell Stackexchange\n\nQ: Are they the same in meaning, the open door and the opened door? What is the difference between \"the open door\" and \"the opened door\"? Are they the same in meaning? Please explain it. Thanks a lot.\n\nA: Open is an adjective. \nOpened is a past participle used as a modifier. \nThe difference in meaning is that open is in contrast to not open. The door  could have been open for ten years or a century. It could always be open. \nOpened expands to The \"having been opened\" door...\nThis refers to the action of the door having been opened. The door used to be shut, now it is open. It has been opened. It has not always been open. It could have been opened ten years ago or two  seconds ago, but at some point it was closed. \n"]], "0_corpus": "stackexchange", "1_conv_id": "9913e8b8325c4550a0bfb3f5298189d2", "1_model_name": "BM25", "1_prompt": "are opened or are open?", "1_output": [["are opened or are open?", "Ell Stackexchange\n\nQ: Are they the same in meaning, the open door and the opened door? What is the difference between \"the open door\" and \"the opened door\"? Are they the same in meaning? Please explain it. Thanks a lot.\n\nA: Open is an adjective. \nOpened is a past participle used as a modifier. \nThe difference in meaning is that open is in contrast to not open. The door  could have been open for ten years or a century. It could always be open. \nOpened expands to The \"having been opened\" door...\nThis refers to the action of the door having been opened. The door used to be shut, now it is open. It has been opened. It has not always been open. It could have been opened ten years ago or two  seconds ago, but at some point it was closed. \n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1722309466.1126, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "bc8a08f1718846e58aa907a16ce9620b", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "where did german immigrants settled in the 1800s", "0_output": [["where did german immigrants settled in the 1800s", "German Americans\n\nA particularly attractive destination was Milwaukee, which came to be known as \"the German Athens\". Radical Germans trained in politics in the old country dominated the city's Socialists. Skilled workers dominated many crafts, while entrepreneurs created the brewing industry; the most famous brands included Pabst, Schlitz, Miller, and Blatz.\nWhereas half of German immigrants settled in cities, the other half established farms in the Midwest. From Ohio to the Plains states, a heavy presence persists in rural areas into the 21st century.\nDeep South\nFew German immigrants settled in the Deep South, apart from New Orleans, the German Coast, and Texas.\nTexas\nTexas attracted many Germans who entered through Galveston and Indianola, both those who came to farm, and later immigrants who more rapidly took industrial jobs in cities such as Houston. As in Milwaukee, Germans in Houston built the brewing industry. By the 1920s, the first generation of college-educated German Americans were moving into the chemical and oil industries.\nTexas had about 20,000 German Americans in the 1850s. They did not form a uniform bloc, but were highly diverse and drew from geographic areas and all sectors of European society, except that very few aristocrats or upper middle class businessmen arrived. In this regard, Texas Germania was a microcosm of the Germania nationwide."]], "0_corpus": "wikipedia", "1_conv_id": "b7cfab5df92f40cb97e749a1057688d3", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": "where did german immigrants settled in the 1800s", "1_output": [["where did german immigrants settled in the 1800s", "Immigration to Germany\n\nImmigration to Germany, both in the country's modern borders and the many political entities that preceded it, has occurred throughout the country's history. Today, Germany is one of the most popular destinations for immigrants in the world, with well over 1 million people moving there each year since 2013. As of 2019, around 13.7 million people living in Germany, or about 17% of the population, are first-generation immigrants."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722310078.9673, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "723e06bd28704a6b90a00a505ecb7438", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": "A new method called ATTEMPT utilizing attentional mixtures of soft prompts for knowledge transfer.", "0_output": [["A new method called ATTEMPT utilizing attentional mixtures of soft prompts for knowledge transfer.", "Title: ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts\n\nAbstract: This work introduces a new multi-task, parameter-efficient language model (LM) tuning method that learns to transfer knowledge across different tasks via a mixture of soft prompts-small prefix embedding vectors pre-trained for different tasks. Our method, called ATTEMPT (ATTEntional Mixtures of Prompt Tuning), obtains source prompts as encodings of large-scale source tasks into a small number of parameters and trains an attention module to interpolate the source prompts and a newly initialized target prompt for every instance in the target task. During training, only the target task prompt and the attention weights, which are shared between tasks in multi-task training, are updated, while the original LM and source prompts are intact. ATTEMPT is highly parameter-efficient (e.g., updates 2,300 times fewer parameters than full fine-tuning) while achieving high task performance using knowledge from high-resource tasks. Moreover, it is modular using pre-trained soft prompts, and can flexibly add or remove source prompts for effective knowledge transfer. Our experimental results across 21 diverse NLP datasets show that ATTEMPT significantly outperforms prompt tuning and outperforms or matches fully fine-tuned or other parameter-efficient tuning approaches that use over ten times more parameters. Finally, ATTEMPT outperforms previous work in few-shot learning settings."]], "0_corpus": "arxiv", "1_conv_id": "6584a2b86688459ca86f0cbfc241df00", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "A new method called ATTEMPT utilizing attentional mixtures of soft prompts for knowledge transfer.", "1_output": [["A new method called ATTEMPT utilizing attentional mixtures of soft prompts for knowledge transfer.", "Title: ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts\n\nAbstract: This work introduces a new multi-task, parameter-efficient language model (LM) tuning method that learns to transfer knowledge across different tasks via a mixture of soft prompts-small prefix embedding vectors pre-trained for different tasks. Our method, called ATTEMPT (ATTEntional Mixtures of Prompt Tuning), obtains source prompts as encodings of large-scale source tasks into a small number of parameters and trains an attention module to interpolate the source prompts and a newly initialized target prompt for every instance in the target task. During training, only the target task prompt and the attention weights, which are shared between tasks in multi-task training, are updated, while the original LM and source prompts are intact. ATTEMPT is highly parameter-efficient (e.g., updates 2,300 times fewer parameters than full fine-tuning) while achieving high task performance using knowledge from high-resource tasks. Moreover, it is modular using pre-trained soft prompts, and can flexibly add or remove source prompts for effective knowledge transfer. Our experimental results across 21 diverse NLP datasets show that ATTEMPT significantly outperforms prompt tuning and outperforms or matches fully fine-tuned or other parameter-efficient tuning approaches that use over ten times more parameters. Finally, ATTEMPT outperforms previous work in few-shot learning settings."]], "1_corpus": "arxiv"}
@@ -19,7 +18,6 @@
 {"tstamp": 1722311101.6953, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "42b61a56dbda4f8fb93f621e039f5ce4", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": "do switches use ip or mac?", "0_output": [["do switches use ip or mac?", "Superuser Stackexchange\n\nQ: IP Address vs MAC addresses why do we need ip addresses if mac address is enough to identify the machine???\n\nA: There are many reasons. For example: MAC address is bound to hardware. It means you can't or rather are not supposed to have the same MAC on different hardware. Imagine that google has to change DNS binding of \"google.com\" to \"MAC\" address each time they change some network card on front-end server.\nAnother reason is that MAC addresses are assigned by producers while IP addresses are assigned by network admins. This allows building hierarchy of addresses that is root of packets routing. This is because way from IP A1.A2.A3.A4 to B1.B2.B3.B4 is easily predictable and way from MAC#1 to MAC#2 isn't.\nNext reason is that MAC is Ethernet-related stuff and there are other network technologies that might not use MAC at all.\n"]], "0_corpus": "stackexchange", "1_conv_id": "ae5bb24c1351491fa7a7bb739099f035", "1_model_name": "BM25", "1_prompt": "do switches use ip or mac?", "1_output": [["do switches use ip or mac?", "Serverfault Stackexchange\n\nQ: Deny Static IP Assignments I am currently using a SonicWall Pro 2040 with DHCP enabled, but only with static IPs mapped to specific MAC addresses. There is no dynamic IP scope being used. Currently, no DHCP request will resolve an IP, as desired.\nHowever, if a server or workstation network interface is still configured with a user-defined, static IP address, it works fine. I would like potential users who assign their machines with static IPs to not work.\nIs there a way to deny these types of connections whose MAC address is not approved? \n\nA: There is no way to configure a DHCP server to deny static IP addresses. If you think about it, there is a direct path between hosts on the network that simply does not go through the SonicWall:\n            SonicWall\n            LAN Port\n                ^\n                |\n                v\nHostA <----> Switch <----> HostB\n\nSo if you want to filter on MAC address to stop HostA from advertising an IP address on the network, you need to do so at the switch. As an example, if your switch is a Cisco, the command to use is switchport port-security.\n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1722311103.6017, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "dd1cd051afc647eabf636fc6055dea05", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": "How can retrieval-based methods be optimized to balance the trade-off between retrieval precision and computational efficiency in large-scale neural information retrieval systems?", "0_output": [["How can retrieval-based methods be optimized to balance the trade-off between retrieval precision and computational efficiency in large-scale neural information retrieval systems?", "Evaluation measures (information retrieval)\n\nSome authors choose to interpolate the  function to reduce the impact of \"wiggles\" in the curve. For example, the PASCAL Visual Object Classes challenge (a benchmark for computer vision object detection) until 2010 computed the average precision by averaging the precision over a set of evenly spaced recall levels {0, 0.1, 0.2, ... 1.0}:\nwhere  is an interpolated precision that takes the maximum precision over all recalls greater than :\n.\nAn alternative is to derive an analytical  function by assuming a particular parametric distribution for the underlying decision values. For example, a binormal precision-recall curve can be obtained by assuming decision values in both classes to follow a Gaussian distribution.\nThe minimum achievable AveP for a given classification task is given by:\nPrecision at k\nFor modern (web-scale) information retrieval, recall is no longer a meaningful metric, as many queries have thousands of relevant documents, and few users will be interested in reading all of them. Precision at k documents (P@k) is still a useful metric (e.g., P@10 or \"Precision at 10\" corresponds to the number of relevant results among the top 10 retrieved documents), but fails to take into account the positions of the relevant documents among the top k.  Another shortcoming is that on a query with fewer relevant results than k, even a perfect system will have a score less than 1.  It is easier to score manually since only the top k results need to be examined to determine if they are relevant or not."]], "0_corpus": "wikipedia", "1_conv_id": "181c322773ac4bf5a3d0c6321237b30a", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "How can retrieval-based methods be optimized to balance the trade-off between retrieval precision and computational efficiency in large-scale neural information retrieval systems?", "1_output": [["How can retrieval-based methods be optimized to balance the trade-off between retrieval precision and computational efficiency in large-scale neural information retrieval systems?", "Information retrieval\n\nInformation retrieval (IR) in computing and information science is the task of identifying and retrieving information system resources that are relevant to an information need.  The information need can be specified in the form of a search query. In the case of document retrieval, queries can be based on full-text or other content-based indexing. Information retrieval is the science of searching for information in a document, searching for documents themselves, and also searching for the metadata that describes data, and for databases of texts, images or sounds.\nAutomated information retrieval systems are used to reduce what has been called information overload. An IR system is a software system that provides access to books, journals and other documents; it also stores and manages those documents. Web search engines are the most visible IR applications."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722311155.3291, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "af5d587baf874dcaa68ca13be655b9ec", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": "Impact of AI chatbots on academic integrity and plagiarism", "0_output": [["Impact of AI chatbots on academic integrity and plagiarism", "Title: Will ChatGPT get you caught? Rethinking of Plagiarism Detection\n\nAbstract: The rise of Artificial Intelligence (AI) technology and its impact on education has been a topic of growing concern in recent years. The new generation AI systems such as chatbots have become more accessible on the Internet and stronger in terms of capabilities. The use of chatbots, particularly ChatGPT, for generating academic essays at schools and colleges has sparked fears among scholars. This study aims to explore the originality of contents produced by one of the most popular AI chatbots, ChatGPT. To this end, two popular plagiarism detection tools were used to evaluate the originality of 50 essays generated by ChatGPT on various topics. Our results manifest that ChatGPT has a great potential to generate sophisticated text outputs without being well caught by the plagiarism check software. In other words, ChatGPT can create content on many topics with high originality as if they were written by someone. These findings align with the recent concerns about students using chatbots for an easy shortcut to success with minimal or no effort. Moreover, ChatGPT was asked to verify if the essays were generated by itself, as an additional measure of plagiarism check, and it showed superior performance compared to the traditional plagiarism-detection tools. The paper discusses the need for institutions to consider appropriate measures to mitigate potential plagiarism issues and advise on the ongoing debate surrounding the impact of AI technology on education. Further implications are discussed in the paper."]], "0_corpus": "arxiv", "1_conv_id": "6912b24d5d0444babee7fd61715355bc", "1_model_name": "text-embedding-004", "1_prompt": "Impact of AI chatbots on academic integrity and plagiarism", "1_output": [["Impact of AI chatbots on academic integrity and plagiarism", "Title: Will ChatGPT get you caught? Rethinking of Plagiarism Detection\n\nAbstract: The rise of Artificial Intelligence (AI) technology and its impact on education has been a topic of growing concern in recent years. The new generation AI systems such as chatbots have become more accessible on the Internet and stronger in terms of capabilities. The use of chatbots, particularly ChatGPT, for generating academic essays at schools and colleges has sparked fears among scholars. This study aims to explore the originality of contents produced by one of the most popular AI chatbots, ChatGPT. To this end, two popular plagiarism detection tools were used to evaluate the originality of 50 essays generated by ChatGPT on various topics. Our results manifest that ChatGPT has a great potential to generate sophisticated text outputs without being well caught by the plagiarism check software. In other words, ChatGPT can create content on many topics with high originality as if they were written by someone. These findings align with the recent concerns about students using chatbots for an easy shortcut to success with minimal or no effort. Moreover, ChatGPT was asked to verify if the essays were generated by itself, as an additional measure of plagiarism check, and it showed superior performance compared to the traditional plagiarism-detection tools. The paper discusses the need for institutions to consider appropriate measures to mitigate potential plagiarism issues and advise on the ongoing debate surrounding the impact of AI technology on education. Further implications are discussed in the paper."]], "1_corpus": "arxiv"}
-{"tstamp": 1722311155.3294, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "c96bbb99cf3546ba97838b73048bd5fa", "0_model_name": "BM25", "0_prompt": "What are the main causes and effects of the Industrial Revolution?", "0_output": [["What are the main causes and effects of the Industrial Revolution?", "Industrial Revolution\n\nPrimitivism Primitivism argues that the Industrial Revolution have created an un-natural frame of society and the world in which humans need to adapt to an un-natural urban landscape in which humans are perpetual cogs without personal autonomy.\nCertain primitivists argue for a return to pre-industrial society, while others argue that technology such as modern medicine, and agriculture are all positive for humanity assuming they are controlled by and serve humanity and have no effect on the natural environment.\nPollution and ecological collapse\nThe Industrial Revolution has been criticised for leading to immense ecological and habitat destruction. It has led to immense decrease in the biodiversity of life on Earth. The Industrial revolution has been said to be inherently unsustainable and will lead to eventual collapse of society, mass hunger, starvation, and resource scarcity.\nThe Anthropocene\nThe Anthropocene is a proposed epoch or mass extinction coming from humanity (anthropo- is the Greek root for humanity). Since the start of the Industrial revolution humanity has permanently changed the Earth, such as immense decrease in biodiversity, and mass extinction caused by the Industrial revolution. The effects include permanent changes to the Earth's atmosphere and soil, forests, the mass destruction of the Industrial revolution has led to catastrophic impacts on the Earth. Most organisms are unable to adapt leading to mass extinction with the remaining undergoing evolutionary rescue, as a result of the Industrial revolution."]], "0_corpus": "wikipedia", "1_conv_id": "babef639f1a24f2a9ac89cb3279d6177", "1_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "1_prompt": "What are the main causes and effects of the Industrial Revolution?", "1_output": [["What are the main causes and effects of the Industrial Revolution?", "De-industrialisation of India\n\nThe fall in the hegemony of Mughals reduced the overall productivity of agriculture and reduced the supply of grains. The grain was the primary consumption good for the Indian workers and was non-tradeable. The reduction in the supply of grain resulted in the rise of its prices. This rise in prices and negative supply shock led to a rise in the nominal wages in the cotton and weaving industry. The increased competition from British cotton and rising nominal wages reduced the profitability of the cotton industry of India. Thus, the negative supply shock in agricultural production is also an important reason behind the de-industrialisation of cotton\u2013industries.\nThe short run as well as long run impact on living standards and growth rate of GDP providing agriculture sector competitive advantage with strengthening of the productivity advance on the land at home or increasing openness to world in turn increases GDP in the short run. The causes of de-industrialisation are region or country specific as in the case of India in the 19th and 20th century. The colonial rule under the British led to the decline of textile and handicrafts industries through their policies and introduction of machine made goods in to the Indian market. Some of the causes of de-industrialisation in India during that period were:\nIntroduction of machine made goods in the Indian subcontinent at a cheaper rate, which led to the decline of the traditional textile industry of India."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722311183.6493, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "344ba32a2d4e44d4857f9fcc603f8ef8", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "What are the differences between machine learning and deep learning?", "0_output": [["What are the differences between machine learning and deep learning?", "Comparison of deep learning software\n\nThe following table compares notable software frameworks, libraries and computer programs for deep learning.\nDeep-learning software by name\nComparison of compatibility of machine learning models"]], "0_corpus": "wikipedia", "1_conv_id": "b3eb01f72a77480caae9a4025986769f", "1_model_name": "voyage-multilingual-2", "1_prompt": "What are the differences between machine learning and deep learning?", "1_output": [["What are the differences between machine learning and deep learning?", "Deep learning\n\nOverview Most modern deep learning models are based on multi-layered neural networks such as convolutional neural networks and transformers, although they can also include propositional formulas or latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep Boltzmann machines.\nFundamentally, deep learning refers to a class of machine learning algorithms in which a hierarchy of layers is used to transform input data into a slightly more abstract and composite representation. For example, in an image recognition model, the raw input may be an image (represented as a tensor of pixels). The first representational layer may attempt to identify basic shapes such as lines and circles, the second layer may compose and encode arrangements of edges, the third layer may encode a nose and eyes, and the fourth layer may recognize that the image contains a face.\nImportantly, a deep learning process can learn which features to optimally place in which level on its own. Prior to deep learning, machine learning techniques often involved hand-crafted feature engineering to transform the data into a more suitable representation for a classification algorithm to operate upon. In the deep learning approach, features are not hand-crafted and the model discovers useful feature representations from the data automatically. This does not eliminate the need for hand-tuning; for example, varying numbers of layers and layer sizes can provide different degrees of abstraction."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722311199.5255, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "92e783c936084f088d8757c497e00510", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": "are electrical outlets on studs?", "0_output": [["are electrical outlets on studs?", "Electronics Stackexchange\n\nQ: Purpose of breadboard \"studs\" on base off micro breadboards? I recently bought some micro breadboards on Ebay.  Extremely useful little things.  However each have two awkward studs on their bases.  See the attached photo.   What I'm curious about is their purpose?  What might these \"plugin\" to or be used for?  I think I'm going to file them off.\n\n\nA: They connect to a base that fixes them with regards to each other.\n\n"]], "0_corpus": "stackexchange", "1_conv_id": "5a38498ae0f7449186feb5beb2f13faf", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": "are electrical outlets on studs?", "1_output": [["are electrical outlets on studs?", "Math Stackexchange\n\nQ: Write $\\cos^2(x)$ as linear combination of $x \\mapsto \\sin(x)$ and $x \\mapsto \\cos(x)$ \nCan we write $\\cos^2(x)$ as linear combination of $x \\mapsto \\sin(x)$ and  $x \\mapsto \\cos(x)$?\n\nI know\n$$\n\\cos^2(x)\n= \\frac{\\cos(2x) + 1}{2}\n= 1 - \\sin^2(x)\n= \\cos(2x) + \\sin^2(x)\n$$\nbut none of these helped.\nThen, I tried to solve\n$$\n\\cos^2(x) = \\alpha \\sin(x) + \\beta \\cos(x)\n$$\nfor the coefficients $\\alpha, \\beta \\in \\mathbb{R}$.\nBut when plugging in $x = 0$ I get $\\beta = 1$ and for $x = \\frac{\\pi}{2}$ I get $\\alpha = 0$. Plugging those values back in I obtain a false statement, and WolframAlpha can't do better!\nThis is from a numerical analysis exam and the second function is $x \\mapsto \\sqrt{2}\\cos\\left(\\frac{\\pi}{4} - x \\right)$, which can easily be expressed in terms of $x \\mapsto \\sin(x)$ and $x \\mapsto \\cos(x)$ by the corresponding addition formula.\n\nA: The function $f(x):=\\cos^2 x$ has $f(x+\\pi)\\equiv f(x)$, but any linear combination $g$ of $\\cos$ and $\\sin$ has $g(x+\\pi)\\equiv -g(x)$.\n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1722311256.1494, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "8685880d067944afa0b8593df3edca7c", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "A paper on scalable and accurate graph transformers using sparse attention and expander graphs", "0_output": [["A paper on scalable and accurate graph transformers using sparse attention and expander graphs", "Title: Exphormer: Sparse Transformers for Graphs\n\nAbstract: Graph transformers have emerged as a promising architecture for a variety of graph learning and representation tasks. Despite their successes, though, it remains challenging to scale graph transformers to large graphs while maintaining accuracy competitive with message-passing networks. In this paper, we introduce Exphormer, a framework for building powerful and scalable graph transformers. Exphormer consists of a sparse attention mechanism based on two mechanisms: virtual global nodes and expander graphs, whose mathematical characteristics, such as spectral expansion, pseduorandomness, and sparsity, yield graph transformers with complexity only linear in the size of the graph, while allowing us to prove desirable theoretical properties of the resulting transformer models. We show that incorporating Exphormer into the recently-proposed GraphGPS framework produces models with competitive empirical results on a wide variety of graph datasets, including state-of-the-art results on three datasets. We also show that Exphormer can scale to datasets on larger graphs than shown in previous graph transformer architectures. Code can be found at \\url{https://github.com/hamed1375/Exphormer}."]], "0_corpus": "arxiv", "1_conv_id": "e515fe1b4bad484eba5701f6c8446cc0", "1_model_name": "intfloat/multilingual-e5-large-instruct", "1_prompt": "A paper on scalable and accurate graph transformers using sparse attention and expander graphs", "1_output": [["A paper on scalable and accurate graph transformers using sparse attention and expander graphs", "Title: Deformable Graph Transformer\n\nAbstract: Transformer-based models have recently shown success in representation learning on graph-structured data beyond natural language processing and computer vision. However, the success is limited to small-scale graphs due to the drawbacks of full dot-product attention on graphs such as the quadratic complexity with respect to the number of nodes and message aggregation from enormous irrelevant nodes. To address these issues, we propose Deformable Graph Transformer (DGT) that performs sparse attention via dynamically sampled relevant nodes for efficiently handling large-scale graphs with a linear complexity in the number of nodes. Specifically, our framework first constructs multiple node sequences with various criteria to consider both structural and semantic proximity. Then, combining with our learnable Katz Positional Encodings, the sparse attention is applied to the node sequences for learning node representations with a significantly reduced computational cost. Extensive experiments demonstrate that our DGT achieves state-of-the-art performance on 7 graph benchmark datasets with 2.5 - 449 times less computational cost compared to transformer-based graph models with full attention."]], "1_corpus": "arxiv"}

data/retrieval_battle-c4bdd700-2c9c-4d9b-92d7-63fecd0c1575.jsonl

@@ -1,25 +1,24 @@
 {"tstamp": 1722436132.3737, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "9fc7884f010949148b0c0372ed6bdc60", "0_model_name": "voyage-multilingual-2", "0_prompt": "are rolls and biscuits the same?", "0_output": [["are rolls and biscuits the same?", "English Stackexchange\n\nQ: Is a ship's biscuit a biscuit or a cake? Cakes go hard when they are stale. Biscuits go soft when they are stale.\nSo, what about a ship's biscuit?\n\nA: From an American point of view, none of the terms biscuit, cake, or cookie is an appropriate descriptor for  ships biscuit, which is also known as hardtack and by other terms.  The image below is from wikipedia's article about hardtack.\n\nThe article says\n\nHardtack (or hard tack) is a simple type of cracker or biscuit, made from flour, water, and sometimes salt\n\nFrom that description and from the picture, cracker evidently is the most appropriate term.\nNote, the previously-linked biscuit article begins by distinguishing American usage (where biscuits are like small breadrolls except often leavened with soda, not yeast) from British usage (where biscuits are like cookies or possibly like crackers).\n"]], "0_corpus": "stackexchange", "1_conv_id": "229c16e4a1cb4ca78bcc68c20a11da00", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": "are rolls and biscuits the same?", "1_output": [["are rolls and biscuits the same?", "Math Stackexchange\n\nQ: Annihilator for a tensor $T\\in\\wedge V^{\\ast}$ For $T\\in\\wedge^{k} V^{\\ast}$ the annihilator is set\n$$an(T)= \\{\\phi\\in V^{\\ast}\\mid \\phi\\wedge T=0\\}$$\nThen I need to prove that $dim(an(T))\\leq k$ and is equal iff $T$ is decomposable ($i.e.$, $T=\\phi_1\\wedge\\cdots \\wedge \\phi_k$ for $\\phi_j \\in V^{\\ast}$).\nI do not know how to attack this problem, suppose that has dimension greater that $k$ but I get no contradiction or at least do not know how to find it.\n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1722436145.3122, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "fbbb80e6e1cd4354a3097f1d997a3063", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": "is chief executive officer capitalized?", "0_output": [["is chief executive officer capitalized?", "English Stackexchange\n\nQ: When should titles like \"captain\" or \"admiral\" be capitalized? Please excuse the contrived example below, but I am wondering if this example is properly capitalised? I'm assuming that capitals are used when addressing someone directly, but lower case is used elsewhere.\nCan someone give some rules on this? \n\nThe captain walked into the room. \n  \"Hi, Captain,\" said the admiral.\n  \"Hi, Sir,\" replied the captain.\n  \"Call me Admiral.\"\n  \"Hi Admiral.\"\n  The captain and admiral stepped forward.\n  \"Prime Minister, this is Captain Jones,\" said Admiral Brown.\n  \"Pleased to meet you,\" said the prime minister.\n\n\nA: At least according to CMOS, there are a few rules that apply. (Your mileage may vary with AP or Oxford rules, I just happen to have CMOS within grabbing distance.)\n7.16 says that titles used in place of names in direct address are capitalized. (e.g., \"'Hi, Captain'\")\n7.17 says that titles used alone in place of a name are generally lowercased (e.g., \"replied the captain\").\n(Edit: Cited the wrong rule.)\n"]], "0_corpus": "stackexchange", "1_conv_id": "18f8939df9cf41d69bd7782b9f3819ce", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "is chief executive officer capitalized?", "1_output": [["is chief executive officer capitalized?", "English Stackexchange\n\nQ: Correct capitalization for \"of\" in an acronym definition For an acronym that includes the word \"of\", do you capitalize its usage within the definition (or expansion) of the acronym\nFor example, in the follow sentence:\nMy coworker Steve suffers from Complete Avoidance of Work Syndrome (CAOWS).\nShould of be written as Of?\n\nA: I wouldn't capitalize the o in \"of\".\nHere's a well known example.\nTOEFL: \n\nTest of English as a Foreign Language.\n\nTypically, prepositions and articles should not be capitalized.\nFor a comprehensive capitalization rule list, see here.\n"]], "1_corpus": "stackexchange"}
-{"tstamp": 1722436325.5236, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "24959d5ccc4c4f3ca75055b9cce2a838", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": "who plays meredith quill in guardians of the galaxy 2", "0_output": [["who plays meredith quill in guardians of the galaxy 2", "List of Marvel Comics characters: Q\n\nMeredith Quill appears in films set in the Marvel Cinematic Universe (MCU), portrayed by Laura Haddock.\nFirst appearing in Guardians of the Galaxy (2014), she gives Peter Quill a mixtape of her favorite songs before dying of terminal cancer.\nMeredith appears in the prologue of Guardians of the Galaxy Vol. 2, in which it is revealed she pursued a relationship with Ego, who secretly gave her the tumor that killed her.\nMeredith appears in Guardians of the Galaxy: The Telltale Series, voiced by Courtenay Taylor. In a flashback, Peter Quill promises her not to use violence if he confronts bullies. Additionally, she previously encountered Yondu, who took in Peter on her advice. Amidst Peter's apparent death, he sees a vision of Meredith being revived by the Eternity Forge. Following Hala the Accuser's defeat, the player has the option to either have Mantis help Peter make contact with his mother's spirit or use the last of the Eternity Forge's energy to revive her.\nMeredith appears in Marvel's Guardians of the Galaxy, voiced by Mylène Dinh-Robic. In a flashback, she gifts Peter his father's guns for his 13th birthday before she is later killed by the Chitauri. Additionally, the Magus uses an illusion of her in a failed attempt to coax Peter into submission."]], "0_corpus": "wikipedia", "1_conv_id": "d94f94041f324178bfb62096f35d40b7", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "who plays meredith quill in guardians of the galaxy 2", "1_output": [["who plays meredith quill in guardians of the galaxy 2", "List of Marvel Comics characters: Q\n\nMeredith Quill appears in films set in the Marvel Cinematic Universe (MCU), portrayed by Laura Haddock.\nFirst appearing in Guardians of the Galaxy (2014), she gives Peter Quill a mixtape of her favorite songs before dying of terminal cancer.\nMeredith appears in the prologue of Guardians of the Galaxy Vol. 2, in which it is revealed she pursued a relationship with Ego, who secretly gave her the tumor that killed her.\nMeredith appears in Guardians of the Galaxy: The Telltale Series, voiced by Courtenay Taylor. In a flashback, Peter Quill promises her not to use violence if he confronts bullies. Additionally, she previously encountered Yondu, who took in Peter on her advice. Amidst Peter's apparent death, he sees a vision of Meredith being revived by the Eternity Forge. Following Hala the Accuser's defeat, the player has the option to either have Mantis help Peter make contact with his mother's spirit or use the last of the Eternity Forge's energy to revive her.\nMeredith appears in Marvel's Guardians of the Galaxy, voiced by Mylène Dinh-Robic. In a flashback, she gifts Peter his father's guns for his 13th birthday before she is later killed by the Chitauri. Additionally, the Magus uses an illusion of her in a failed attempt to coax Peter into submission."]], "1_corpus": "wikipedia"}
-{"tstamp": 1722436384.9754, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "cde58fd409d44b1b8bd0c8490be0b24c", "0_model_name": "text-embedding-3-large", "0_prompt": "who played charlie barosky on sons of anarchy", "0_output": [["who played charlie barosky on sons of anarchy", "Sons of Anarchy\n\nQ'orianka Kilcher as Kerrianne Larkin-Telford (season 3), Chibs and Fiona's daughter, and Jimmy O's stepdaughter\nMonique Gabriela Curnen as Amelia Dominguez (season 3), the caretaker of Gemma's father Nate\nMichael Fairman as 'Lumpy' Feldstein (season 3), a boxing club owner in Charming\nBob McCracken as Brendan Roarke (seasons 4 & 6–7; guest season 3), a member of the True IRA council\nFrank Potter as Eric Miles (season 4; co-star season 3), a SAMCRO prospect\nWalter Wong as Chris 'V-Lin' Von Lin (seasons 4–6), a SAMCRO prospect\nDanny Trejo as Romero 'Romeo' Parada (season 4; special guest season 5), a high-ranking member of the Galindo Cartel, who is also a C.I.A. agent\nTimothy V. Murphy as Galen O'Shay (seasons 4–6), a leader within the True IRA\nMerle Dandridge as Rita Roosevelt (seasons 4–5), the wife of San Joaquin Sheriff Eli Roosevelt\nBenito Martinez as Luis Torres (season 4; special guest season 5), a member of the Galindo Cartel\nDavid Rees Snell as Agent Grad Nicholas (season 4), an ATF agent\nBilly Brown as August Marks (seasons 5–7), an Oakland drug kingpin and crime boss who works with Damon Pope\nReynaldo Gallegos as 'Fiasco' (seasons 5–7)\nHarold Perrineau as Damon Pope (season 5), an Oakland gangster who oversees the Niners"]], "0_corpus": "wikipedia", "1_conv_id": "738aa5d219da4ac0a96e0e194a92de30", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "who played charlie barosky on sons of anarchy", "1_output": [["who played charlie barosky on sons of anarchy", "Peter Weller\n\nIn 2013, he played the part of the villain in the remake of the \"Hookman\" episode of the original Hawaii Five-O. The part was originally played in 1973 by Jay J. Armes. In seasons six and seven of Sons of Anarchy, he plays the ruthless ex-cop Charles Barosky, a boss at the Stockton port and has dealings with the SAMCRO motorcycle club.\nIn 2017, Weller began appearing as Dr. Paul Vellek, a scientist, on The Last Ship.\nDirecting\nWeller has directed various projects for television, including episodes of Homicide: Life on the Street, Monk and three episodes of Odyssey 5. He directed a 1997 adaptation of Elmore Leonard's Gold Coast starring David Caruso and Marg Helgenberger after previously starring in a version of Leonard's thriller Cat Chaser (1989). He has directed eleven episodes of the series Sons of Anarchy, two in season 4, titled \"Dorylus\" and \"Hands\", three in season 5, titled \"Authority Vested\", \"Toad's Wild Ride\" and \"Darthy\", three in season 6, titled \"One One Six\", \"Los Fantasmas\" and \"Aon Rud Pearsanta\", and three in season 7, titled \"Some Strange Eruption\", \"What a Piece of Work Is Man\" and \"Suits of Woe\". He has directed one episode each season of Longmire since 2012."]], "1_corpus": "wikipedia"}
+{"tstamp": 1722436325.5236, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "24959d5ccc4c4f3ca75055b9cce2a838", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": "who plays meredith quill in guardians of the galaxy 2", "0_output": [["who plays meredith quill in guardians of the galaxy 2", "List of Marvel Comics characters: Q\n\nMeredith Quill appears in films set in the Marvel Cinematic Universe (MCU), portrayed by Laura Haddock.\nFirst appearing in Guardians of the Galaxy (2014), she gives Peter Quill a mixtape of her favorite songs before dying of terminal cancer.\nMeredith appears in the prologue of Guardians of the Galaxy Vol. 2, in which it is revealed she pursued a relationship with Ego, who secretly gave her the tumor that killed her.\nMeredith appears in Guardians of the Galaxy: The Telltale Series, voiced by Courtenay Taylor. In a flashback, Peter Quill promises her not to use violence if he confronts bullies. Additionally, she previously encountered Yondu, who took in Peter on her advice. Amidst Peter's apparent death, he sees a vision of Meredith being revived by the Eternity Forge. Following Hala the Accuser's defeat, the player has the option to either have Mantis help Peter make contact with his mother's spirit or use the last of the Eternity Forge's energy to revive her.\nMeredith appears in Marvel's Guardians of the Galaxy, voiced by Myl\u00e8ne Dinh-Robic. In a flashback, she gifts Peter his father's guns for his 13th birthday before she is later killed by the Chitauri. Additionally, the Magus uses an illusion of her in a failed attempt to coax Peter into submission."]], "0_corpus": "wikipedia", "1_conv_id": "d94f94041f324178bfb62096f35d40b7", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "who plays meredith quill in guardians of the galaxy 2", "1_output": [["who plays meredith quill in guardians of the galaxy 2", "List of Marvel Comics characters: Q\n\nMeredith Quill appears in films set in the Marvel Cinematic Universe (MCU), portrayed by Laura Haddock.\nFirst appearing in Guardians of the Galaxy (2014), she gives Peter Quill a mixtape of her favorite songs before dying of terminal cancer.\nMeredith appears in the prologue of Guardians of the Galaxy Vol. 2, in which it is revealed she pursued a relationship with Ego, who secretly gave her the tumor that killed her.\nMeredith appears in Guardians of the Galaxy: The Telltale Series, voiced by Courtenay Taylor. In a flashback, Peter Quill promises her not to use violence if he confronts bullies. Additionally, she previously encountered Yondu, who took in Peter on her advice. Amidst Peter's apparent death, he sees a vision of Meredith being revived by the Eternity Forge. Following Hala the Accuser's defeat, the player has the option to either have Mantis help Peter make contact with his mother's spirit or use the last of the Eternity Forge's energy to revive her.\nMeredith appears in Marvel's Guardians of the Galaxy, voiced by Myl\u00e8ne Dinh-Robic. In a flashback, she gifts Peter his father's guns for his 13th birthday before she is later killed by the Chitauri. Additionally, the Magus uses an illusion of her in a failed attempt to coax Peter into submission."]], "1_corpus": "wikipedia"}
+{"tstamp": 1722436384.9754, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "cde58fd409d44b1b8bd0c8490be0b24c", "0_model_name": "text-embedding-3-large", "0_prompt": "who played charlie barosky on sons of anarchy", "0_output": [["who played charlie barosky on sons of anarchy", "Sons of Anarchy\n\nQ'orianka Kilcher as Kerrianne Larkin-Telford (season 3), Chibs and Fiona's daughter, and Jimmy O's stepdaughter\nMonique Gabriela Curnen as Amelia Dominguez (season 3), the caretaker of Gemma's father Nate\nMichael Fairman as 'Lumpy' Feldstein (season 3), a boxing club owner in Charming\nBob McCracken as Brendan Roarke (seasons 4 & 6\u20137; guest season 3), a member of the True IRA council\nFrank Potter as Eric Miles (season 4; co-star season 3), a SAMCRO prospect\nWalter Wong as Chris 'V-Lin' Von Lin (seasons 4\u20136), a SAMCRO prospect\nDanny Trejo as Romero 'Romeo' Parada (season 4; special guest season 5), a high-ranking member of the Galindo Cartel, who is also a C.I.A. agent\nTimothy V. Murphy as Galen O'Shay (seasons 4\u20136), a leader within the True IRA\nMerle Dandridge as Rita Roosevelt (seasons 4\u20135), the wife of San Joaquin Sheriff Eli Roosevelt\nBenito Martinez as Luis Torres (season 4; special guest season 5), a member of the Galindo Cartel\nDavid Rees Snell as Agent Grad Nicholas (season 4), an ATF agent\nBilly Brown as August Marks (seasons 5\u20137), an Oakland drug kingpin and crime boss who works with Damon Pope\nReynaldo Gallegos as 'Fiasco' (seasons 5\u20137)\nHarold Perrineau as Damon Pope (season 5), an Oakland gangster who oversees the Niners"]], "0_corpus": "wikipedia", "1_conv_id": "738aa5d219da4ac0a96e0e194a92de30", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "who played charlie barosky on sons of anarchy", "1_output": [["who played charlie barosky on sons of anarchy", "Peter Weller\n\nIn 2013, he played the part of the villain in the remake of the \"Hookman\" episode of the original Hawaii Five-O. The part was originally played in 1973 by Jay J. Armes. In seasons six and seven of Sons of Anarchy, he plays the ruthless ex-cop Charles Barosky, a boss at the Stockton port and has dealings with the SAMCRO motorcycle club.\nIn 2017, Weller began appearing as Dr. Paul Vellek, a scientist, on The Last Ship.\nDirecting\nWeller has directed various projects for television, including episodes of Homicide: Life on the Street, Monk and three episodes of Odyssey 5. He directed a 1997 adaptation of Elmore Leonard's Gold Coast starring David Caruso and Marg Helgenberger after previously starring in a version of Leonard's thriller Cat Chaser (1989). He has directed eleven episodes of the series Sons of Anarchy, two in season 4, titled \"Dorylus\" and \"Hands\", three in season 5, titled \"Authority Vested\", \"Toad's Wild Ride\" and \"Darthy\", three in season 6, titled \"One One Six\", \"Los Fantasmas\" and \"Aon Rud Pearsanta\", and three in season 7, titled \"Some Strange Eruption\", \"What a Piece of Work Is Man\" and \"Suits of Woe\". He has directed one episode each season of Longmire since 2012."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722436760.2442, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "b3a4545fe4f0454ca94e9c4bf5d00ccc", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "Explain MTEB scores and their relevance in real-life applications.", "0_output": [["Explain MTEB scores and their relevance in real-life applications.", "Title: Dynamical Quantum Processes of Molecular Beams at Surfaces: Dissociative Adsorption of Hydrogen on Metal Surfaces\n\nAbstract: Due to the improvement of computer power and the development of efficient algorithms it is now possible to combine high-dimensional quantum dynamical calculations of the dissociative adsorption of molecular beams with reliable ab-initio potential energy surfaces (PES). In this brief review two recent examples of such studies of the systems H_2/Cu(111), where adsorption is hindered by a noticeable energy barrier, and H_2/Pd(100), where activated as well as non-activated paths to adsorption exist, are presented. The effect of lateral surface corrugations on the sticking probability in the tunneling and the classical regime and the role of additional parallel momentum are discussed in the context of the H_2/Cu(111) results. For the system H_2/Pd(100) it is shown that the initial decrease of the sticking probability with increasing kinetic energy, which is usually attributed to a precursor mechanism, can be explained by dynamical steering. In addition, the influence of rotation on the adsorption and desorption dynamics is examined."]], "0_corpus": "arxiv", "1_conv_id": "7d81e64c2cb04a4193cbf4ee3119ef85", "1_model_name": "text-embedding-3-large", "1_prompt": "Explain MTEB scores and their relevance in real-life applications.", "1_output": [["Explain MTEB scores and their relevance in real-life applications.", "Title: MTEB: Massive Text Embedding Benchmark\n\nAbstract: Text embeddings are commonly evaluated on a small set of datasets from a single task not covering their possible applications to other tasks. It is unclear whether state-of-the-art embeddings on semantic textual similarity (STS) can be equally well applied to other tasks like clustering or reranking. This makes progress in the field difficult to track, as various models are constantly being proposed without proper evaluation. To solve this problem, we introduce the Massive Text Embedding Benchmark (MTEB). MTEB spans 8 embedding tasks covering a total of 58 datasets and 112 languages. Through the benchmarking of 33 models on MTEB, we establish the most comprehensive benchmark of text embeddings to date. We find that no particular text embedding method dominates across all tasks. This suggests that the field has yet to converge on a universal text embedding method and scale it up sufficiently to provide state-of-the-art results on all embedding tasks. MTEB comes with open-source code and a public leaderboard at https://github.com/embeddings-benchmark/mteb."]], "1_corpus": "arxiv"}
 {"tstamp": 1722436858.2756, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "c3e05bfd38a54334966b3fee56c50372", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": "What is a landmark paper that captions images with sentiments?", "0_output": [["What is a landmark paper that captions images with sentiments?", "Title: SentiCap: Generating Image Descriptions with Sentiments\n\nAbstract: The recent progress on image recognition and language modeling is making automatic description of image content a reality. However, stylized, non-factual aspects of the written description are missing from the current systems. One such style is descriptions with emotions, which is commonplace in everyday communication, and influences decision-making and interpersonal relationships. We design a system to describe an image with emotions, and present a model that automatically generates captions with positive or negative sentiments. We propose a novel switching recurrent neural network with word-level regularization, which is able to produce emotional image captions using only 2000+ training sentences containing sentiments. We evaluate the captions with different automatic and crowd-sourcing metrics. Our model compares favourably in common quality metrics for image captioning. In 84.6% of cases the generated positive captions were judged as being at least as descriptive as the factual captions. Of these positive captions 88% were confirmed by the crowd-sourced workers as having the appropriate sentiment."]], "0_corpus": "arxiv", "1_conv_id": "2c277a48c058469a99f7d8e871e56114", "1_model_name": "text-embedding-004", "1_prompt": "What is a landmark paper that captions images with sentiments?", "1_output": [["What is a landmark paper that captions images with sentiments?", "Title: SentiCap: Generating Image Descriptions with Sentiments\n\nAbstract: The recent progress on image recognition and language modeling is making automatic description of image content a reality. However, stylized, non-factual aspects of the written description are missing from the current systems. One such style is descriptions with emotions, which is commonplace in everyday communication, and influences decision-making and interpersonal relationships. We design a system to describe an image with emotions, and present a model that automatically generates captions with positive or negative sentiments. We propose a novel switching recurrent neural network with word-level regularization, which is able to produce emotional image captions using only 2000+ training sentences containing sentiments. We evaluate the captions with different automatic and crowd-sourcing metrics. Our model compares favourably in common quality metrics for image captioning. In 84.6% of cases the generated positive captions were judged as being at least as descriptive as the factual captions. Of these positive captions 88% were confirmed by the crowd-sourced workers as having the appropriate sentiment."]], "1_corpus": "arxiv"}
-{"tstamp": 1722438411.1567, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "d78c6a77faa9427f879a69e311af8ff9", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "how to velvet beef", "0_output": [["how to velvet beef", "Beef aging\n\nBeef aging or ageing is a process of preparing beef for consumption by aging it, in order to break down the connective tissue within the meat."]], "0_corpus": "wikipedia", "1_conv_id": "cb6b594524cf4ab5b886defe195e06aa", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_prompt": "how to velvet beef", "1_output": [["how to velvet beef", "Beef\n\nFrying Meat can be cooked in boiling oil, typically by shallow frying, although deep frying may be used, often for meat enrobed with breadcrumbs as in milanesas or finger steaks. Larger pieces such as steaks may be cooked this way, or meat may be cut smaller as in stir frying, typically an Asian way of cooking: cooking oil with flavorings such as garlic, ginger and onions is put in a very hot wok. Then small pieces of meat are added, followed by ingredients which cook more quickly, such as mixed vegetables. The dish is ready when the ingredients are 'just cooked'.\nMoist heat\nMoist heat cooking methods include braising, pot roasting, stewing and sous-vide. These techniques are often used for cuts of beef that are tougher, as these longer, lower-temperature cooking methods have time to dissolve connecting tissue which otherwise makes meat remain tough after cooking.\nStewing or simmering\nsimmering meat, whole or cut into bite-size pieces, in a water-based liquid with flavorings. This technique may be used as part of pressure cooking.\nBraising\ncooking meats, in a covered container, with small amounts of liquids (usually seasoned or flavored). Unlike stewing, braised meat is not fully immersed in liquid, and usually is browned before the oven step.\nSous-vide\nSous-vide, French for \"under vacuum\", is a method of cooking food sealed in airtight plastic bags in a water bath for a long time—72 hours is not unknown—at an accurately determined temperature much lower than normally used for other types of cooking. The intention is to maintain the integrity of ingredients and achieve very precise control of cooking. Although water is used in the method, only moisture in or added to the food bags is in contact with the food."]], "1_corpus": "wikipedia"}
-{"tstamp": 1722438494.5451, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "07e1aabf8adb4a288b137072ddca886d", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": "What's the purpose of `if __name__ == '__main__'`?", "0_output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: Why does it do this ? if - __name__ == '__main__' \nDuplicate of:\nWhat does if __name__== \"__main__\" do? \n\nConsider this code:\nif __name__ == '__main__':\n    import pdb\n    pdb.run(\"interact()\\n\")\n\nWhat does the following line mean?\nif(__name__=='__main__')\n\nI fainted.\n\nA: That is a check to see if you are directly running the script or if it is included in a library.\nWhen you run a python script like this:\npython myScript.py\n\nIt sends a parameter, telling you to run the programs first method, which is widely called \"main\", so when __name__ is __main__ you know that the program was executed from a command line or double clicked.\n"]], "0_corpus": "stackexchange", "1_conv_id": "8dbcfb951f86457b93498918f768052c", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "What's the purpose of `if __name__ == '__main__'`?", "1_output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: What does `if name == \"__main__\"` mean in Python? \nPossible Duplicate:\nWhat does <if name==“main”:> do? \n\nI have wrote scripts in Python for quite a while now and I study more of Python as I need it. When reading other people's code I meet if name == \"__main__\": construct quite often. \nWhat is it good for?\n\nA: Basically, \nThere's a distinction between the \"main\" script file and external files which were imported or referenced in another way. If the script is the \"main\" script then the special variable __name__ will equal \"__main__\".\nYou can use this to protect the \"execution\" code from the classes and variables the script has. This enables you to import a script and use classes and variables without actually running that script's main code if it has any.\nSee also: What does if name == “main”: do?\n"]], "1_corpus": "stackexchange"}
-{"tstamp": 1722438547.9969, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "20fcbcf3531d433693a2c8fd215d20fa", "0_model_name": "voyage-multilingual-2", "0_prompt": "Find me a planet near α 23h 06m 29.368s and δ −05° 02′ 29.04″ that could be habitable.", "0_output": [["Find me a planet near α 23h 06m 29.368s and δ −05° 02′ 29.04″ that could be habitable.", "Alpha Centauri\n\nIn 2009, computer simulations showed that a planet might have been able to form near the inner edge of Alpha Centauri B's habitable zone, which extends from  from the star. Certain special assumptions, such as considering that the Alpha Centauri pair may have initially formed with a wider separation and later moved closer to each other (as might be possible if they formed in a dense star cluster), would permit an accretion-friendly environment farther from the star. Bodies around Alpha Centauri A would be able to orbit at slightly farther distances due to its stronger gravity. In addition, the lack of any brown dwarfs or gas giants in close orbits around Alpha Centauri make the likelihood of terrestrial planets greater than otherwise. A theoretical study indicates that a radial velocity analysis might detect a hypothetical planet of  in Alpha Centauri B's habitable zone.\nRadial velocity measurements of Alpha Centauri B made with the High Accuracy Radial Velocity Planet Searcher spectrograph were sufficiently sensitive to detect a  planet within the habitable zone of the star (i.e. with an orbital period P = 200 days), but no planets were detected.\nCurrent estimates place the probability of finding an Earth-like planet around Alpha Centauri at roughly 75%. The observational thresholds for planet detection in the habitable zones by the radial velocity method are currently (2017) estimated to be about  for Alpha Centauri A,  for Alpha Centauri B, and  for Proxima Centauri.\nEarly computer-generated models of planetary formation predicted the existence of terrestrial planets around both Alpha Centauri A and B, but most recent numerical investigations have shown that the gravitational pull of the companion star renders the accretion of planets difficult. Despite these difficulties, given the similarities to the Sun in spectral types, star type, age and probable stability of the orbits, it has been suggested that this stellar system could hold one of the best possibilities for harbouring extraterrestrial life on a potential planet."]], "0_corpus": "wikipedia", "1_conv_id": "c47d106e79854ed2997f0d261f2f5c71", "1_model_name": "text-embedding-004", "1_prompt": "Find me a planet near α 23h 06m 29.368s and δ −05° 02′ 29.04″ that could be habitable.", "1_output": [["Find me a planet near α 23h 06m 29.368s and δ −05° 02′ 29.04″ that could be habitable.", "TRAPPIST-1e\n\nTRAPPIST-1e, also designated as 2MASS J23062928-0502285 e, is a rocky, close-to-Earth-sized exoplanet orbiting within the habitable zone around the ultracool dwarf star TRAPPIST-1, located  away from Earth in the constellation of Aquarius. Astronomers used the transit method to find the exoplanet, a method that measures the dimming of a star when a planet crosses in front of it.\nThe exoplanet was one of seven discovered orbiting the star using observations from the Spitzer Space Telescope. Three of the seven (e, f, and g) are in the habitable zone or the \"goldilocks zone\". TRAPPIST-1e is similar to Earth's mass, radius, density, gravity, temperature, and stellar flux. It is also confirmed that TRAPPIST-1e lacks a cloud-free hydrogen-dominated atmosphere, meaning it is more likely to have a compact atmosphere like the terrestrial planets in the Solar System.\nIn November 2018, researchers determined that of the seven exoplanets in the multi-planetary system, TRAPPIST-1e has the best chance of being an Earth-like ocean planet, and the one most worthy of further study regarding habitability. According to the Habitable Exoplanets Catalog, TRAPPIST-1e is among the best potentially habitable exoplanets discovered.\nPhysical characteristics\nMass, radius, composition and temperature"]], "1_corpus": "wikipedia"}
+{"tstamp": 1722438411.1567, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "d78c6a77faa9427f879a69e311af8ff9", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "how to velvet beef", "0_output": [["how to velvet beef", "Beef aging\n\nBeef aging or ageing is a process of preparing beef for consumption by aging it, in order to break down the connective tissue within the meat."]], "0_corpus": "wikipedia", "1_conv_id": "cb6b594524cf4ab5b886defe195e06aa", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_prompt": "how to velvet beef", "1_output": [["how to velvet beef", "Beef\n\nFrying Meat can be cooked in boiling oil, typically by shallow frying, although deep frying may be used, often for meat enrobed with breadcrumbs as in milanesas or finger steaks. Larger pieces such as steaks may be cooked this way, or meat may be cut smaller as in stir frying, typically an Asian way of cooking: cooking oil with flavorings such as garlic, ginger and onions is put in a very hot wok. Then small pieces of meat are added, followed by ingredients which cook more quickly, such as mixed vegetables. The dish is ready when the ingredients are 'just cooked'.\nMoist heat\nMoist heat cooking methods include braising, pot roasting, stewing and sous-vide. These techniques are often used for cuts of beef that are tougher, as these longer, lower-temperature cooking methods have time to dissolve connecting tissue which otherwise makes meat remain tough after cooking.\nStewing or simmering\nsimmering meat, whole or cut into bite-size pieces, in a water-based liquid with flavorings. This technique may be used as part of pressure cooking.\nBraising\ncooking meats, in a covered container, with small amounts of liquids (usually seasoned or flavored). Unlike stewing, braised meat is not fully immersed in liquid, and usually is browned before the oven step.\nSous-vide\nSous-vide, French for \"under vacuum\", is a method of cooking food sealed in airtight plastic bags in a water bath for a long time\u201472 hours is not unknown\u2014at an accurately determined temperature much lower than normally used for other types of cooking. The intention is to maintain the integrity of ingredients and achieve very precise control of cooking. Although water is used in the method, only moisture in or added to the food bags is in contact with the food."]], "1_corpus": "wikipedia"}
+{"tstamp": 1722438494.5451, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "07e1aabf8adb4a288b137072ddca886d", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": "What's the purpose of `if __name__ == '__main__'`?", "0_output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: Why does it do this ? if - __name__ == '__main__' \nDuplicate of:\nWhat does if __name__== \"__main__\" do? \n\nConsider this code:\nif __name__ == '__main__':\n    import pdb\n    pdb.run(\"interact()\\n\")\n\nWhat does the following line mean?\nif(__name__=='__main__')\n\nI fainted.\n\nA: That is a check to see if you are directly running the script or if it is included in a library.\nWhen you run a python script like this:\npython myScript.py\n\nIt sends a parameter, telling you to run the programs first method, which is widely called \"main\", so when __name__ is __main__ you know that the program was executed from a command line or double clicked.\n"]], "0_corpus": "stackexchange", "1_conv_id": "8dbcfb951f86457b93498918f768052c", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "What's the purpose of `if __name__ == '__main__'`?", "1_output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: What does `if name == \"__main__\"` mean in Python? \nPossible Duplicate:\nWhat does <if name==\u201cmain\u201d:> do? \n\nI have wrote scripts in Python for quite a while now and I study more of Python as I need it. When reading other people's code I meet if name == \"__main__\": construct quite often. \nWhat is it good for?\n\nA: Basically, \nThere's a distinction between the \"main\" script file and external files which were imported or referenced in another way. If the script is the \"main\" script then the special variable __name__ will equal \"__main__\".\nYou can use this to protect the \"execution\" code from the classes and variables the script has. This enables you to import a script and use classes and variables without actually running that script's main code if it has any.\nSee also: What does if name == \u201cmain\u201d: do?\n"]], "1_corpus": "stackexchange"}
+{"tstamp": 1722438547.9969, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "20fcbcf3531d433693a2c8fd215d20fa", "0_model_name": "voyage-multilingual-2", "0_prompt": "Find me a planet near \u03b1 23h 06m 29.368s and \u03b4 \u221205\u00b0 02\u2032 29.04\u2033 that could be habitable.", "0_output": [["Find me a planet near \u03b1 23h 06m 29.368s and \u03b4 \u221205\u00b0 02\u2032 29.04\u2033 that could be habitable.", "Alpha Centauri\n\nIn 2009, computer simulations showed that a planet might have been able to form near the inner edge of Alpha Centauri B's habitable zone, which extends from  from the star. Certain special assumptions, such as considering that the Alpha Centauri pair may have initially formed with a wider separation and later moved closer to each other (as might be possible if they formed in a dense star cluster), would permit an accretion-friendly environment farther from the star. Bodies around Alpha Centauri A would be able to orbit at slightly farther distances due to its stronger gravity. In addition, the lack of any brown dwarfs or gas giants in close orbits around Alpha Centauri make the likelihood of terrestrial planets greater than otherwise. A theoretical study indicates that a radial velocity analysis might detect a hypothetical planet of  in Alpha Centauri B's habitable zone.\nRadial velocity measurements of Alpha Centauri B made with the High Accuracy Radial Velocity Planet Searcher spectrograph were sufficiently sensitive to detect a  planet within the habitable zone of the star (i.e. with an orbital period P = 200 days), but no planets were detected.\nCurrent estimates place the probability of finding an Earth-like planet around Alpha Centauri at roughly 75%. The observational thresholds for planet detection in the habitable zones by the radial velocity method are currently (2017) estimated to be about  for Alpha Centauri A,  for Alpha Centauri B, and  for Proxima Centauri.\nEarly computer-generated models of planetary formation predicted the existence of terrestrial planets around both Alpha Centauri A and B, but most recent numerical investigations have shown that the gravitational pull of the companion star renders the accretion of planets difficult. Despite these difficulties, given the similarities to the Sun in spectral types, star type, age and probable stability of the orbits, it has been suggested that this stellar system could hold one of the best possibilities for harbouring extraterrestrial life on a potential planet."]], "0_corpus": "wikipedia", "1_conv_id": "c47d106e79854ed2997f0d261f2f5c71", "1_model_name": "text-embedding-004", "1_prompt": "Find me a planet near \u03b1 23h 06m 29.368s and \u03b4 \u221205\u00b0 02\u2032 29.04\u2033 that could be habitable.", "1_output": [["Find me a planet near \u03b1 23h 06m 29.368s and \u03b4 \u221205\u00b0 02\u2032 29.04\u2033 that could be habitable.", "TRAPPIST-1e\n\nTRAPPIST-1e, also designated as 2MASS J23062928-0502285 e, is a rocky, close-to-Earth-sized exoplanet orbiting within the habitable zone around the ultracool dwarf star TRAPPIST-1, located  away from Earth in the constellation of Aquarius. Astronomers used the transit method to find the exoplanet, a method that measures the dimming of a star when a planet crosses in front of it.\nThe exoplanet was one of seven discovered orbiting the star using observations from the Spitzer Space Telescope. Three of the seven (e, f, and g) are in the habitable zone or the \"goldilocks zone\". TRAPPIST-1e is similar to Earth's mass, radius, density, gravity, temperature, and stellar flux. It is also confirmed that TRAPPIST-1e lacks a cloud-free hydrogen-dominated atmosphere, meaning it is more likely to have a compact atmosphere like the terrestrial planets in the Solar System.\nIn November 2018, researchers determined that of the seven exoplanets in the multi-planetary system, TRAPPIST-1e has the best chance of being an Earth-like ocean planet, and the one most worthy of further study regarding habitability. According to the Habitable Exoplanets Catalog, TRAPPIST-1e is among the best potentially habitable exoplanets discovered.\nPhysical characteristics\nMass, radius, composition and temperature"]], "1_corpus": "wikipedia"}
 {"tstamp": 1722439019.271, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "32a38d7fae954f8db724ef0a1306fe9f", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "what is the capital of france", "0_output": [["what is the capital of france", "List of capitals of France\n\nThis is a chronological list of capitals of France. The capital of France has been Paris since its liberation in 1944."]], "0_corpus": "wikipedia", "1_conv_id": "c389f4c4fb7541ac86e3ed02f2291042", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "what is the capital of france", "1_output": [["what is the capital of france", "List of capitals of France\n\nThis is a chronological list of capitals of France. The capital of France has been Paris since its liberation in 1944."]], "1_corpus": "wikipedia"}
-{"tstamp": 1722439070.133, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "1260d56ada9a43e981ea9e442c8f5495", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "where was ismail kadare born", "0_output": [["where was ismail kadare born", "Ismail Kadare\n\nIsmail Kadare (; 28 January 1936 – 1 July 2024) was an Albanian novelist, poet, essayist, screenwriter, and playwright. He was a leading international literary figure and intellectual. He focused on poetry until the publication of his first novel, The General of the Dead Army, which made him famous internationally.\nKadare is regarded by some as one of the greatest writers and intellectuals of the 20th and 21st centuries, and as a universal voice against totalitarianism. Living in Albania during a time of strict censorship, he devised stratagems to outwit Communist censors who had banned three of his books, using devices such as parable, myth, fable, folk-tale, allegory, and legend, sprinkled with double-entendre, allusion, insinuation, satire, and coded messages. In 1990, to escape the Communist regime and its Sigurimi secret police, he defected to Paris. From the 1990s he was asked by both major political parties in Albania to become a consensual President of the country, but declined. In 1996, France made him a foreign associate of the , and in 2016, he was a Commandeur de la Légion d'Honneur recipient."]], "0_corpus": "wikipedia", "1_conv_id": "db3b40e3d9774a709df8f539dae94774", "1_model_name": "GritLM/GritLM-7B", "1_prompt": "where was ismail kadare born", "1_output": [["where was ismail kadare born", "Ismail Kadare\n\nEarly life and education Ismail Kadare was born on 28 January 1936, in the Kingdom of Albania during the reign of King Zog I. He was born in Gjirokastër, an historic Ottoman Empire fortress mountain city of tall stone houses in southern Albania, a dozen miles from the border with Greece. He lived there on a crooked, narrow street known as Lunatics' Lane.\nIsmail's parents were Halit Kadare, a post office employee, and Hatixhe Dobi, a homemaker, who had married in 1933 when she was 17. On his mother's side, his great-grandfather was a Bejtexhi of the Bektashi Order, known as Hoxhë Dobi. Though he was born into a Muslim family, he was an atheist.\nThree years after Kadare was born, Italian Prime Minister Benito Mussolini's troops invaded Albania and ousted the king. Italian rule followed. He was nine years old when the Italian troops were withdrawn, and the communist-led People's Socialist Republic of Albania was established.\nKadare attended primary and secondary schools in Gjirokastër. He then studied Languages and Literature at the Faculty of History and Philology of the University of Tirana. In 1956, he received a teacher's diploma. He lived in Tirana until moving to France in 1990.\nLiterary career\nEarly\nAt age 11, Kadare read William Shakespeare's play Macbeth. He recalled years later: \"Because I did not yet understand that I could simply purchase it in a bookstore, I copied much of it by hand and took it home. My childhood imagination pushed me to feel like a co-author of the play.\""]], "1_corpus": "wikipedia"}
+{"tstamp": 1722439070.133, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "1260d56ada9a43e981ea9e442c8f5495", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "where was ismail kadare born", "0_output": [["where was ismail kadare born", "Ismail Kadare\n\nIsmail Kadare (; 28 January 1936 \u2013 1 July 2024) was an Albanian novelist, poet, essayist, screenwriter, and playwright. He was a leading international literary figure and intellectual. He focused on poetry until the publication of his first novel, The General of the Dead Army, which made him famous internationally.\nKadare is regarded by some as one of the greatest writers and intellectuals of the 20th and 21st centuries, and as a universal voice against totalitarianism. Living in Albania during a time of strict censorship, he devised stratagems to outwit Communist censors who had banned three of his books, using devices such as parable, myth, fable, folk-tale, allegory, and legend, sprinkled with double-entendre, allusion, insinuation, satire, and coded messages. In 1990, to escape the Communist regime and its Sigurimi secret police, he defected to Paris. From the 1990s he was asked by both major political parties in Albania to become a consensual President of the country, but declined. In 1996, France made him a foreign associate of the , and in 2016, he was a Commandeur de la L\u00e9gion d'Honneur recipient."]], "0_corpus": "wikipedia", "1_conv_id": "db3b40e3d9774a709df8f539dae94774", "1_model_name": "GritLM/GritLM-7B", "1_prompt": "where was ismail kadare born", "1_output": [["where was ismail kadare born", "Ismail Kadare\n\nEarly life and education Ismail Kadare was born on 28 January 1936, in the Kingdom of Albania during the reign of King Zog I. He was born in Gjirokast\u00ebr, an historic Ottoman Empire fortress mountain city of tall stone houses in southern Albania, a dozen miles from the border with Greece. He lived there on a crooked, narrow street known as Lunatics' Lane.\nIsmail's parents were Halit Kadare, a post office employee, and Hatixhe Dobi, a homemaker, who had married in 1933 when she was 17. On his mother's side, his great-grandfather was a Bejtexhi of the Bektashi Order, known as Hoxh\u00eb Dobi. Though he was born into a Muslim family, he was an atheist.\nThree years after Kadare was born, Italian Prime Minister Benito Mussolini's troops invaded Albania and ousted the king. Italian rule followed. He was nine years old when the Italian troops were withdrawn, and the communist-led People's Socialist Republic of Albania was established.\nKadare attended primary and secondary schools in Gjirokast\u00ebr. He then studied Languages and Literature at the Faculty of History and Philology of the University of Tirana. In 1956, he received a teacher's diploma. He lived in Tirana until moving to France in 1990.\nLiterary career\nEarly\nAt age 11, Kadare read William Shakespeare's play Macbeth. He recalled years later: \"Because I did not yet understand that I could simply purchase it in a bookstore, I copied much of it by hand and took it home. My childhood imagination pushed me to feel like a co-author of the play.\""]], "1_corpus": "wikipedia"}
 {"tstamp": 1722439202.6928, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "91b5c09508374591a1cc1ab11e7960d0", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": "what the difference between mega charizard x and y?", "0_output": [["what the difference between mega charizard x and y?", "Gaming Stackexchange\n\nQ: What are the differences between Pokemon X and Pokemon Y? I want to know what are the differences between version X and Y, like pokemons, places, items, megaevolutions, etc.\n\nA: Pokemon Exclusive to Version X\n\n\n*\n\n*Mega Charizard X\n\n*Mega Mewtwo X\n\n*Staryu/Starmie\n\n*Pinsir\n\n*Houndour/Houndoom\n\n*Poochyena/Mightyena\n\n*Aron/Lairon/Aggron\n\n*Sawk\n\n*Swirlix/Slurpuff\n\n*Clauncher/Clawitzer\n\n*Xerneas\n\n\nPokemon Exclusive to Version Y\n\n\n*\n\n*Mega Charizard Y\n\n*Mega Mewtwo Y\n\n*Shellder/Cloyster\n\n*Heracross\n\n*Larvitar/Pupitar/Tyranitar\n\n*Electrike/Manectric\n\n*Purrloin/Liepard\n\n*Throh\n\n*Spritzee/Aromatisse\n\n*Skrelp/Dragalge\n\n*Yveltal\n\n\nSource - Serebii\nIt's also been noted that there are minor story differences:\n\nWithout giving away too much, both legendaries do end up having an impact on the story, so it's a matter of preference as to which one you want to see.\n\nSource: Shack News\nAs always the main thing you should consider when choosing between Pokemon X and Pokemon Y is which version exclusive Pokemon appeal to you the most.\n"]], "0_corpus": "stackexchange", "1_conv_id": "66440ef005a64e6ca18305711af5bb40", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "what the difference between mega charizard x and y?", "1_output": [["what the difference between mega charizard x and y?", "Gaming Stackexchange\n\nQ: What are the differences between Pokemon X and Pokemon Y? I want to know what are the differences between version X and Y, like pokemons, places, items, megaevolutions, etc.\n\nA: Pokemon Exclusive to Version X\n\n\n*\n\n*Mega Charizard X\n\n*Mega Mewtwo X\n\n*Staryu/Starmie\n\n*Pinsir\n\n*Houndour/Houndoom\n\n*Poochyena/Mightyena\n\n*Aron/Lairon/Aggron\n\n*Sawk\n\n*Swirlix/Slurpuff\n\n*Clauncher/Clawitzer\n\n*Xerneas\n\n\nPokemon Exclusive to Version Y\n\n\n*\n\n*Mega Charizard Y\n\n*Mega Mewtwo Y\n\n*Shellder/Cloyster\n\n*Heracross\n\n*Larvitar/Pupitar/Tyranitar\n\n*Electrike/Manectric\n\n*Purrloin/Liepard\n\n*Throh\n\n*Spritzee/Aromatisse\n\n*Skrelp/Dragalge\n\n*Yveltal\n\n\nSource - Serebii\nIt's also been noted that there are minor story differences:\n\nWithout giving away too much, both legendaries do end up having an impact on the story, so it's a matter of preference as to which one you want to see.\n\nSource: Shack News\nAs always the main thing you should consider when choosing between Pokemon X and Pokemon Y is which version exclusive Pokemon appeal to you the most.\n"]], "1_corpus": "stackexchange"}
-{"tstamp": 1722439222.3324, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "5478714bcf9c4b018c0f9baa922a7738", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "mcpe how to split items?", "0_output": [["mcpe how to split items?", "Gaming Stackexchange\n\nQ: How to divide item stack in MC PE? I have been struggling with this for a while and couldn’t find a clear answer online so I thought I’d post it since I couldn’t find a correct answer anywhere. \nHow can I divide a stack in my inventory without switching to classic mode? Unable to right click or hold shift since it is on a smartphone. Some answers I found said something about a coin icon on the left but I don’t see it. \n\nA: To divide a stack press and hold on it until a green bar starts filling right above the item. The amount the green bar fills is the amount that is being selected. If half the bar is filled then half the stack is selected. Release the hold when the green bar fills to your desired amount. Tap an empty space in your inventory to move the selected items to the new slot.\n"]], "0_corpus": "stackexchange", "1_conv_id": "e8b8f1b269f24c9abed6a72f410a46bb", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "mcpe how to split items?", "1_output": [["mcpe how to split items?", "Stackoverflow Stackexchange\n\nQ: split items in list How can I turn the following list\n['1','2','A,B,C,D','7','8']\n\ninto\n['1','2','A','B','C','D','7','8']\nin the most pythonic way?\nI have very unpythonic code that creates nested list, and then flatterens:\nsum ( [ word.split(',') for word in words ], [] )\n\n\nA: In [1]: from itertools import chain\n\nIn [2]: lis=['1','2','A,B,C,D','7','8']\n\n\nIn [5]: list(chain(*(x.split(',') for x in lis)))\nOut[5]: ['1', '2', 'A', 'B', 'C', 'D', '7', '8']\n\nto further reduce the unwanted split() calls:\nIn [7]: list(chain(*(x.split(',') if ',' in x else x for x in lis)))\nOut[7]: ['1', '2', 'A', 'B', 'C', 'D', '7', '8']\n\nusing map():\nIn [8]: list(chain(*map(lambda x:x.split(','),lis)))\nOut[8]: ['1', '2', 'A', 'B', 'C', 'D', '7', '8']\n\nIn [9]: list(chain(*map(lambda x:x.split(',') if ',' in x else x,lis)))\nOut[9]: ['1', '2', 'A', 'B', 'C', 'D', '7', '8']\n\n"]], "1_corpus": "stackexchange"}
+{"tstamp": 1722439222.3324, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "5478714bcf9c4b018c0f9baa922a7738", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "mcpe how to split items?", "0_output": [["mcpe how to split items?", "Gaming Stackexchange\n\nQ: How to divide item stack in MC PE? I have been struggling with this for a while and couldn\u2019t find a clear answer online so I thought I\u2019d post it since I couldn\u2019t find a correct answer anywhere. \nHow can I divide a stack in my inventory without switching to classic mode? Unable to right click or hold shift since it is on a smartphone. Some answers I found said something about a coin icon on the left but I don\u2019t see it. \n\nA: To divide a stack press and hold on it until a green bar starts filling right above the item. The amount the green bar fills is the amount that is being selected. If half the bar is filled then half the stack is selected. Release the hold when the green bar fills to your desired amount. Tap an empty space in your inventory to move the selected items to the new slot.\n"]], "0_corpus": "stackexchange", "1_conv_id": "e8b8f1b269f24c9abed6a72f410a46bb", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "mcpe how to split items?", "1_output": [["mcpe how to split items?", "Stackoverflow Stackexchange\n\nQ: split items in list How can I turn the following list\n['1','2','A,B,C,D','7','8']\n\ninto\n['1','2','A','B','C','D','7','8']\nin the most pythonic way?\nI have very unpythonic code that creates nested list, and then flatterens:\nsum ( [ word.split(',') for word in words ], [] )\n\n\nA: In [1]: from itertools import chain\n\nIn [2]: lis=['1','2','A,B,C,D','7','8']\n\n\nIn [5]: list(chain(*(x.split(',') for x in lis)))\nOut[5]: ['1', '2', 'A', 'B', 'C', 'D', '7', '8']\n\nto further reduce the unwanted split() calls:\nIn [7]: list(chain(*(x.split(',') if ',' in x else x for x in lis)))\nOut[7]: ['1', '2', 'A', 'B', 'C', 'D', '7', '8']\n\nusing map():\nIn [8]: list(chain(*map(lambda x:x.split(','),lis)))\nOut[8]: ['1', '2', 'A', 'B', 'C', 'D', '7', '8']\n\nIn [9]: list(chain(*map(lambda x:x.split(',') if ',' in x else x,lis)))\nOut[9]: ['1', '2', 'A', 'B', 'C', 'D', '7', '8']\n\n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1722439233.6545, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "40415139651e41369a555cbf1b059530", "0_model_name": "voyage-multilingual-2", "0_prompt": "Survey examining models like ELMo and BERT and their impact on contextual word representations.", "0_output": [["Survey examining models like ELMo and BERT and their impact on contextual word representations.", "Title: A Survey on Contextual Embeddings\n\nAbstract: Contextual embeddings, such as ELMo and BERT, move beyond global word representations like Word2Vec and achieve ground-breaking performance on a wide range of natural language processing tasks. Contextual embeddings assign each word a representation based on its context, thereby capturing uses of words across varied contexts and encoding knowledge that transfers across languages. In this survey, we review existing contextual embedding models, cross-lingual polyglot pre-training, the application of contextual embeddings in downstream tasks, model compression, and model analyses."]], "0_corpus": "arxiv", "1_conv_id": "352b6a0075ce4dad8b290e0ae9ac7048", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "Survey examining models like ELMo and BERT and their impact on contextual word representations.", "1_output": [["Survey examining models like ELMo and BERT and their impact on contextual word representations.", "Title: A Survey on Contextual Embeddings\n\nAbstract: Contextual embeddings, such as ELMo and BERT, move beyond global word representations like Word2Vec and achieve ground-breaking performance on a wide range of natural language processing tasks. Contextual embeddings assign each word a representation based on its context, thereby capturing uses of words across varied contexts and encoding knowledge that transfers across languages. In this survey, we review existing contextual embedding models, cross-lingual polyglot pre-training, the application of contextual embeddings in downstream tasks, model compression, and model analyses."]], "1_corpus": "arxiv"}
-{"tstamp": 1722439246.7796, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "0673117b825548babc52ab352f07e856", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "how much older is arwen than aragorn?", "0_output": [["how much older is arwen than aragorn?", "Scifi Stackexchange\n\nQ: How old was Lady Arwen, wife of Aragorn in the Lord of the Rings? Lady Arwen is possibly thousands of years old when The Lord of the Rings starts off. But exactly how old was she? What was the age difference between her and Aragorn? Considering the age difference, would not Aragorn seem to be a small kid in front of her?\n\nA: The Tale of Years (Appendix B) has this entry in the Third Age:\n\n241 Birth of Arwen Undómiel.\n\nSo Arwen was 2,777 years old in TA3018 when the War of the Ring happened.\nElrond makes exactly your point when Aragorn first falls in love with her:\n\n\"But as for Arwen the Fair, Lady of Imladris and of Lórien, Evenstar of her people, she is of lineage greater than yours, and she has lived in the world already so long that to her you are but as a yearling shoot beside a young birch of many summers. She is too far above you. And so, I think, it may well seem to her.\"\n\nBut she does fall in love with him anyway.\n"]], "0_corpus": "stackexchange", "1_conv_id": "fbc8618cec6047e6815c508db80bce51", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "how much older is arwen than aragorn?", "1_output": [["how much older is arwen than aragorn?", "Scifi Stackexchange\n\nQ: How old was Lady Arwen, wife of Aragorn in the Lord of the Rings? Lady Arwen is possibly thousands of years old when The Lord of the Rings starts off. But exactly how old was she? What was the age difference between her and Aragorn? Considering the age difference, would not Aragorn seem to be a small kid in front of her?\n\nA: The Tale of Years (Appendix B) has this entry in the Third Age:\n\n241 Birth of Arwen Undómiel.\n\nSo Arwen was 2,777 years old in TA3018 when the War of the Ring happened.\nElrond makes exactly your point when Aragorn first falls in love with her:\n\n\"But as for Arwen the Fair, Lady of Imladris and of Lórien, Evenstar of her people, she is of lineage greater than yours, and she has lived in the world already so long that to her you are but as a yearling shoot beside a young birch of many summers. She is too far above you. And so, I think, it may well seem to her.\"\n\nBut she does fall in love with him anyway.\n"]], "1_corpus": "stackexchange"}
+{"tstamp": 1722439246.7796, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "0673117b825548babc52ab352f07e856", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "how much older is arwen than aragorn?", "0_output": [["how much older is arwen than aragorn?", "Scifi Stackexchange\n\nQ: How old was Lady Arwen, wife of Aragorn in the Lord of the Rings? Lady Arwen is possibly thousands of years old when The Lord of the Rings starts off. But exactly how old was she? What was the age difference between her and Aragorn? Considering the age difference, would not Aragorn seem to be a small kid in front of her?\n\nA: The Tale of Years (Appendix B) has this entry in the Third Age:\n\n241 Birth of Arwen Und\u00f3miel.\n\nSo Arwen was 2,777 years old in TA3018 when the War of the Ring happened.\nElrond makes exactly your point when Aragorn first falls in love with her:\n\n\"But as for Arwen the Fair, Lady of Imladris and of L\u00f3rien, Evenstar of her people, she is of lineage greater than yours, and she has lived in the world already so long that to her you are but as a yearling shoot beside a young birch of many summers. She is too far above you. And so, I think, it may well seem to her.\"\n\nBut she does fall in love with him anyway.\n"]], "0_corpus": "stackexchange", "1_conv_id": "fbc8618cec6047e6815c508db80bce51", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "how much older is arwen than aragorn?", "1_output": [["how much older is arwen than aragorn?", "Scifi Stackexchange\n\nQ: How old was Lady Arwen, wife of Aragorn in the Lord of the Rings? Lady Arwen is possibly thousands of years old when The Lord of the Rings starts off. But exactly how old was she? What was the age difference between her and Aragorn? Considering the age difference, would not Aragorn seem to be a small kid in front of her?\n\nA: The Tale of Years (Appendix B) has this entry in the Third Age:\n\n241 Birth of Arwen Und\u00f3miel.\n\nSo Arwen was 2,777 years old in TA3018 when the War of the Ring happened.\nElrond makes exactly your point when Aragorn first falls in love with her:\n\n\"But as for Arwen the Fair, Lady of Imladris and of L\u00f3rien, Evenstar of her people, she is of lineage greater than yours, and she has lived in the world already so long that to her you are but as a yearling shoot beside a young birch of many summers. She is too far above you. And so, I think, it may well seem to her.\"\n\nBut she does fall in love with him anyway.\n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1722439271.9889, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "56f96663188a4324a3626ff5721d318d", "0_model_name": "voyage-multilingual-2", "0_prompt": "answer of do you mind?", "0_output": [["answer of do you mind?", "English Stackexchange\n\nQ: Answering the question: Do you mind if...? The following always puzzles me as a non-native speaker. \nWhen somebody asks the question \"Do you mind if...\", there seem to be two possible responses. \n\n\n*\n\n*\"Sure\" and \"No, not at all\", which both mean that the person doesn't mind.\n\n*\"Actually, I do mind\", which means that the person does mind.\n\n\nWhy is this so confusing? Especially, how come people reply \"Sure\" to this question, if that could be understood to mean that they for sure do mind?\n\nA: \"Sure\" isn't answering the question as asked; it's answering an implied question, namely: \"is it OK with you if...\".\n\"No, not at all\" is answering the question, taken literally.\n"]], "0_corpus": "stackexchange", "1_conv_id": "13b6abf21ddb4697b0e775567e0d2427", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "answer of do you mind?", "1_output": [["answer of do you mind?", "Ell Stackexchange\n\nQ: 'Would you mind' and 'Do you mind' Does the auxiliary verb 'do' work rather than the modal 'would' in the example below in the sense of permission:\n\nWould you mind speaking in English?\n\n\nA: \nWould you mind speaking in English?\n\nis a polite request.\n\nDo you mind speaking in English?\n\ncomes across more harshly, and implies you are being inconvenienced somehow by not hearing English. A bit softer than something like:\n\nDo you mind, I am talking on the telephone.\n\nthat you might say when someone tries to interrupt you.\n"]], "1_corpus": "stackexchange"}
-{"tstamp": 1722439308.3951, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "87e3074b66bc40b8a4e18100bf698de1", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "What's the difference between type 1 and type 2 diabetes?", "0_output": [["What's the difference between type 1 and type 2 diabetes?", "Type 2 diabetes\n\nType 2 diabetes makes up about 90% of cases of diabetes, with the other 10% due primarily to type 1 diabetes and gestational diabetes. In type 1 diabetes there is a lower total level of insulin to control blood glucose, due to an autoimmune induced loss of insulin-producing beta cells in the pancreas. Diagnosis of diabetes is by blood tests such as fasting plasma glucose, oral glucose tolerance test, or glycated hemoglobin (A1C).\nType 2 diabetes is largely preventable by staying at a normal weight, exercising regularly, and eating a healthy diet (high in fruits and vegetables and low in sugar and saturated fats). Treatment involves exercise and dietary changes. If blood sugar levels are not adequately lowered, the medication metformin is typically recommended. Many people may eventually also require insulin injections. In those on insulin, routinely checking blood sugar levels (such as through a continuous glucose monitor) is advised; however, this may not be needed in those who are not on insulin therapy. Bariatric surgery often improves diabetes in those who are obese.\nRates of type 2 diabetes have increased markedly since 1960 in parallel with obesity. As of 2015 there were approximately 392 million people diagnosed with the disease compared to around 30 million in 1985. Typically it begins in middle or older age, although rates of type 2 diabetes are increasing in young people. Type 2 diabetes is associated with a ten-year-shorter life expectancy. Diabetes was one of the first diseases ever described, dating back to an Egyptian manuscript from  BCE. The importance of insulin in the disease was determined in the 1920s."]], "0_corpus": "wikipedia", "1_conv_id": "d8744acb01ae4a82943648ca94d79516", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "What's the difference between type 1 and type 2 diabetes?", "1_output": [["What's the difference between type 1 and type 2 diabetes?", "Type 2 diabetes\n\nType 2 diabetes makes up about 90% of cases of diabetes, with the other 10% due primarily to type 1 diabetes and gestational diabetes. In type 1 diabetes there is a lower total level of insulin to control blood glucose, due to an autoimmune induced loss of insulin-producing beta cells in the pancreas. Diagnosis of diabetes is by blood tests such as fasting plasma glucose, oral glucose tolerance test, or glycated hemoglobin (A1C).\nType 2 diabetes is largely preventable by staying at a normal weight, exercising regularly, and eating a healthy diet (high in fruits and vegetables and low in sugar and saturated fats). Treatment involves exercise and dietary changes. If blood sugar levels are not adequately lowered, the medication metformin is typically recommended. Many people may eventually also require insulin injections. In those on insulin, routinely checking blood sugar levels (such as through a continuous glucose monitor) is advised; however, this may not be needed in those who are not on insulin therapy. Bariatric surgery often improves diabetes in those who are obese.\nRates of type 2 diabetes have increased markedly since 1960 in parallel with obesity. As of 2015 there were approximately 392 million people diagnosed with the disease compared to around 30 million in 1985. Typically it begins in middle or older age, although rates of type 2 diabetes are increasing in young people. Type 2 diabetes is associated with a ten-year-shorter life expectancy. Diabetes was one of the first diseases ever described, dating back to an Egyptian manuscript from  BCE. The importance of insulin in the disease was determined in the 1920s."]], "1_corpus": "wikipedia"}
-{"tstamp": 1722439322.3316, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "fd1078f44978481e81ad953c74850d2d", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": "how to determine read only domain controller?", "0_output": [["how to determine read only domain controller?", "Serverfault Stackexchange\n\nQ: Registry key to tell if a server is a Read Only Domain Controller Is there a registry key I can check to tell if a server is a read only domain controller?\n\nA: I've now got a code snippet to check if the EnableRSOForRODC key exists, and if it does, returns it value.\n1>NUL: 2>NUL: REG QUERY \"HKEY_LOCAL_MACHINE\\SYSTEM\\CurrentControlSet\\Services\\DNS\\Parameters\" /v \"EnableRSOFor‌​RODC\" || @Goto NoRODCExists\nFOR /F \"tokens=3 skip=1 \" %%G IN ('REG QUERY \"HKEY_LOCAL_MACHINE\\SYSTEM\\CurrentControlSet\\Services\\DNS\\Parameters\" /v \"EnableRSOFor‌​RODC\"') DO (Echo Read Only Domain Controller? %%G.)\n:NoRODCExists\n\n"]], "0_corpus": "stackexchange", "1_conv_id": "f57c7e49da8d486d8013be860e652408", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_prompt": "how to determine read only domain controller?", "1_output": [["how to determine read only domain controller?", "Stackoverflow Stackexchange\n\nQ: Why does gzip_disable make nginx crash? I've inspired my nginx configuration file from defunkt's conf file for unicorn but it seems that the line gzip_disable \"MSIE [1-6]\\.\"; makes everything crash. I get the error that this site is temporarily unavailable (served from nginx/html/50x.html). Commenting out the line makes everything work again, fiddling with the regexp doesn't change a thing.\nI'm running nginx v.1.0.10 and ubuntu 11.10.\nAny idea?\n\nA: It's already about few years when Nginx API changed for this directive, it should be:\ngzip_disable              \"msie6\";\n\nFull stack Nginx+Unicorn optimized configuration can be found on the gist.\n"]], "1_corpus": "stackexchange"}
+{"tstamp": 1722439308.3951, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "87e3074b66bc40b8a4e18100bf698de1", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "What's the difference between type 1 and type 2 diabetes?", "0_output": [["What's the difference between type 1 and type 2 diabetes?", "Type 2 diabetes\n\nType 2 diabetes makes up about 90% of cases of diabetes, with the other 10% due primarily to type 1 diabetes and gestational diabetes. In type 1 diabetes there is a lower total level of insulin to control blood glucose, due to an autoimmune induced loss of insulin-producing beta cells in the pancreas. Diagnosis of diabetes is by blood tests such as fasting plasma glucose, oral glucose tolerance test, or glycated hemoglobin (A1C).\nType\u00a02 diabetes is largely preventable by staying at a normal weight, exercising regularly, and eating a healthy diet (high in fruits and vegetables and low in sugar and saturated fats). Treatment involves exercise and dietary changes. If blood sugar levels are not adequately lowered, the medication metformin is typically recommended. Many people may eventually also require insulin injections. In those on insulin, routinely checking blood sugar levels (such as through a continuous glucose monitor) is advised; however, this may not be needed in those who are not on insulin therapy. Bariatric surgery often improves diabetes in those who are obese.\nRates of type\u00a02 diabetes have increased markedly since 1960 in parallel with obesity. As of 2015 there were approximately 392\u00a0million people diagnosed with the disease compared to around 30\u00a0million in 1985. Typically it begins in middle or older age, although rates of type 2 diabetes are increasing in young people. Type 2 diabetes is associated with a ten-year-shorter life expectancy. Diabetes was one of the first diseases ever described, dating back to an Egyptian manuscript from  BCE. The importance of insulin in the disease was determined in the 1920s."]], "0_corpus": "wikipedia", "1_conv_id": "d8744acb01ae4a82943648ca94d79516", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "What's the difference between type 1 and type 2 diabetes?", "1_output": [["What's the difference between type 1 and type 2 diabetes?", "Type 2 diabetes\n\nType 2 diabetes makes up about 90% of cases of diabetes, with the other 10% due primarily to type 1 diabetes and gestational diabetes. In type 1 diabetes there is a lower total level of insulin to control blood glucose, due to an autoimmune induced loss of insulin-producing beta cells in the pancreas. Diagnosis of diabetes is by blood tests such as fasting plasma glucose, oral glucose tolerance test, or glycated hemoglobin (A1C).\nType\u00a02 diabetes is largely preventable by staying at a normal weight, exercising regularly, and eating a healthy diet (high in fruits and vegetables and low in sugar and saturated fats). Treatment involves exercise and dietary changes. If blood sugar levels are not adequately lowered, the medication metformin is typically recommended. Many people may eventually also require insulin injections. In those on insulin, routinely checking blood sugar levels (such as through a continuous glucose monitor) is advised; however, this may not be needed in those who are not on insulin therapy. Bariatric surgery often improves diabetes in those who are obese.\nRates of type\u00a02 diabetes have increased markedly since 1960 in parallel with obesity. As of 2015 there were approximately 392\u00a0million people diagnosed with the disease compared to around 30\u00a0million in 1985. Typically it begins in middle or older age, although rates of type 2 diabetes are increasing in young people. Type 2 diabetes is associated with a ten-year-shorter life expectancy. Diabetes was one of the first diseases ever described, dating back to an Egyptian manuscript from  BCE. The importance of insulin in the disease was determined in the 1920s."]], "1_corpus": "wikipedia"}
+{"tstamp": 1722439322.3316, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "fd1078f44978481e81ad953c74850d2d", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": "how to determine read only domain controller?", "0_output": [["how to determine read only domain controller?", "Serverfault Stackexchange\n\nQ: Registry key to tell if a server is a Read Only Domain Controller Is there a registry key I can check to tell if a server is a read only domain controller?\n\nA: I've now got a code snippet to check if the EnableRSOForRODC key exists, and if it does, returns it value.\n1>NUL: 2>NUL: REG QUERY \"HKEY_LOCAL_MACHINE\\SYSTEM\\CurrentControlSet\\Services\\DNS\\Parameters\" /v \"EnableRSOFor\u200c\u200bRODC\" || @Goto NoRODCExists\nFOR /F \"tokens=3 skip=1 \" %%G IN ('REG QUERY \"HKEY_LOCAL_MACHINE\\SYSTEM\\CurrentControlSet\\Services\\DNS\\Parameters\" /v \"EnableRSOFor\u200c\u200bRODC\"') DO (Echo Read Only Domain Controller? %%G.)\n:NoRODCExists\n\n"]], "0_corpus": "stackexchange", "1_conv_id": "f57c7e49da8d486d8013be860e652408", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_prompt": "how to determine read only domain controller?", "1_output": [["how to determine read only domain controller?", "Stackoverflow Stackexchange\n\nQ: Why does gzip_disable make nginx crash? I've inspired my nginx configuration file from defunkt's conf file for unicorn but it seems that the line gzip_disable \"MSIE [1-6]\\.\"; makes everything crash. I get the error that this site is temporarily unavailable (served from nginx/html/50x.html). Commenting out the line makes everything work again, fiddling with the regexp doesn't change a thing.\nI'm running nginx v.1.0.10 and ubuntu 11.10.\nAny idea?\n\nA: It's already about few years when Nginx API changed for this directive, it should be:\ngzip_disable              \"msie6\";\n\nFull stack Nginx+Unicorn optimized configuration can be found on the gist.\n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1722439330.0461, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "3ac2a4bac6a84180886180fa2fbdd044", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": "I am looking for a paper to help me deal with data constraints as I scale up my language model.", "0_output": [["I am looking for a paper to help me deal with data constraints as I scale up my language model.", "Title: Scaling Data-Constrained Language Models\n\nAbstract: The current trend of scaling language models involves increasing both parameter count and training dataset size. Extrapolating this trend suggests that training dataset size may soon be limited by the amount of text data available on the internet. Motivated by this limit, we investigate scaling language models in data-constrained regimes. Specifically, we run a large set of experiments varying the extent of data repetition and compute budget, ranging up to 900 billion training tokens and 9 billion parameter models. We find that with constrained data for a fixed compute budget, training with up to 4 epochs of repeated data yields negligible changes to loss compared to having unique data. However, with more repetition, the value of adding compute eventually decays to zero. We propose and empirically validate a scaling law for compute optimality that accounts for the decreasing value of repeated tokens and excess parameters. Finally, we experiment with approaches mitigating data scarcity, including augmenting the training dataset with code data or removing commonly used filters. Models and datasets from our 400 training runs are freely available at https://github.com/huggingface/datablations."]], "0_corpus": "arxiv", "1_conv_id": "14d6b080bb2849c1a8693559f7e256e9", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "I am looking for a paper to help me deal with data constraints as I scale up my language model.", "1_output": [["I am looking for a paper to help me deal with data constraints as I scale up my language model.", "Title: Scaling Data-Constrained Language Models\n\nAbstract: The current trend of scaling language models involves increasing both parameter count and training dataset size. Extrapolating this trend suggests that training dataset size may soon be limited by the amount of text data available on the internet. Motivated by this limit, we investigate scaling language models in data-constrained regimes. Specifically, we run a large set of experiments varying the extent of data repetition and compute budget, ranging up to 900 billion training tokens and 9 billion parameter models. We find that with constrained data for a fixed compute budget, training with up to 4 epochs of repeated data yields negligible changes to loss compared to having unique data. However, with more repetition, the value of adding compute eventually decays to zero. We propose and empirically validate a scaling law for compute optimality that accounts for the decreasing value of repeated tokens and excess parameters. Finally, we experiment with approaches mitigating data scarcity, including augmenting the training dataset with code data or removing commonly used filters. Models and datasets from our 400 training runs are freely available at https://github.com/huggingface/datablations."]], "1_corpus": "arxiv"}
 {"tstamp": 1722439330.6484, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "b9180981787a4de48a889a11da7b12d4", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": "can initial velocity be negative?", "0_output": [["can initial velocity be negative?", "Physics Stackexchange\n\nQ: Negative vectors (e.g. velocity) If you said someone had a velocity of $-12\\,{\\rm mph}$ and they were traveling north? Wouldn't it mean that they were traveling $12\\,{\\rm mph}$ south?\nThis is a quote from here:\n\nif something [object-x] moving to the right was taken to have positive momentum, then one should consider something [object-y] moving to the left to have negative momentum.\n\nBut isn't momentum a vector, so the direction should be specified separately to the number. What I mean is, object-y with $-1200\\,{\\rm kg\\, m/s}$ and object-x with 1200kg m/s should both have momentum in the same direction. But this can't be because as the quote says they are moving in opposite direction.\n\nA: That quote is abit misleading, momentum is a vector, however a vector is neither negative nor positive, only its components can have this characteristic. The two objects you are describing does not have the same momentum, but they have the same magnitdue of momentum (length of vector).\n"]], "0_corpus": "stackexchange", "1_conv_id": "e4d62363634b479291ca97b9c6289d27", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_prompt": "can initial velocity be negative?", "1_output": [["can initial velocity be negative?", "Superuser Stackexchange\n\nQ: Windows 7 cmd.exe \"text mode\" with Alt+Enter? In XP, I can go to text mode in cmd.exe with alt+enter. This is when it goes full screen (no windows, no graphics - it uploads a raster font to the hardware). No longer works in Windows 7. What happened to text mode? Can I get it back in Windows 7 via some other way? \nIf not, to be able to mourn, what is the API change that makes it impossible and when did it ship?\nedit I've read Why doesn't the Windows command prompt window maximize to the full screen size? but that question is about maximizing the graphical mode console window. My question is different, as I'm asking about the different text mode (a different hw mode).\n\nA: I believe that the difference is that the WDDM (windows display driver model) drivers introduced with Vista and later have no full-screen support, where the XP display drivers does.\n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1722439346.0319, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "efb7d875f2ee4d5f84010b406aa9b5c3", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "do shields count as armor skyrim?", "0_output": [["do shields count as armor skyrim?", "Stackoverflow Stackexchange\n\nQ: C# pass a null value as a generic object rather than a type for overloaded methods I'm working with some legacy C# code and below are two overloaded methods that I can't change:\nvoid PerformCheck(LegacyData data) {...}\nvoid PerformCheck(object data) {...}\n\nThere is some code that uses the above overloaded methods.  When that code passes anything but a LegacyData reference, the PerformCheck(object data) gets invoked, e.g. PerformCheck(\"Hello World\");  However, if null is passed, PerformCheck(LegacyData data) gets invoked.  Strangely the PerformCheck implementations are different depending on what is passed.  I would like the PerformCheck(null) to invoke the PerformCheck(object data) implementation instead.  How do I make this work?\n\nA: You can force the behavior by casting null to anything other than LegacyData.\nvar x = new Test();\nx.PerformCheck((object)null);\n\npublic class Test\n{\n    public void PerformCheck(LegacyData data) { Console.WriteLine(\"legacy\"); }\n    public void PerformCheck(object data) { Console.WriteLine(\"other\"); }\n}\n    \npublic class LegacyData {}\n\nThis outputs \"other\" as expected.\n"]], "0_corpus": "stackexchange", "1_conv_id": "4fa6fe8bd9644a39955c4d7f4dcc09a5", "1_model_name": "GritLM/GritLM-7B", "1_prompt": "do shields count as armor skyrim?", "1_output": [["do shields count as armor skyrim?", "Gaming Stackexchange\n\nQ: In Skyrim, what does a full armor set entail? I.E., do I have to have a shield, or can I just have the helmet, armor, gauntlets, and boots?\n\nA: A full armor set is simply is entirely based on around worn pieces, such as helmet, armor, gauntlets, boots, etc. Shields are considered separate pieces of armor, hence why they are placed within the \"Block\" skill and not the \"Heavy/Light Armor\" skills.\n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1722439356.8617, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "5296aeb678084e99a677d71b97c85d42", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "what is the difference between chkdsk /f and chkdsk /r?", "0_output": [["what is the difference between chkdsk /f and chkdsk /r?", "Superuser Stackexchange\n\nQ: What is the difference between chkdsk /f and chkdsk /r? \n/F              Fixes errors on the disk.\n/R              Locates bad sectors and recovers readable information (implies /F).\n\n(from dir /?)\nIt just sounds like the same thing to me.  I never know which one to use - is there even a difference?  Or is it maybe for backwards compatibility with previous versions of Windows where there was a difference?\n\nA: chkdsk /r does the same thing as chkdsk /f only it also checks for bad sectors on the disk and recovers any readable information. Running chkdsk /r implies that also chkdsk /f is run.\nchkdsk /f only checks for disk errors, not bad sectors.\nMicrosoft has a detailed page for chkdsk. The following is a snippet explaining /f and /r parameters.\n\nFixing disk errors:\nChkdsk corrects disk errors only if you specify the /f command-line\n  option. Chkdsk must be able to lock the drive to correct errors.\n  Because repairs usually change a disk's file allocation table and\n  sometimes cause a loss of data, chkdsk sends a confirmation message.\nFinding physical disk errors:\nUse the /r command-line option to find physical disk errors in the\n  file system.\n\n"]], "0_corpus": "stackexchange", "1_conv_id": "26243994a6974ab099c38f50a8302a08", "1_model_name": "BM25", "1_prompt": "what is the difference between chkdsk /f and chkdsk /r?", "1_output": [["what is the difference between chkdsk /f and chkdsk /r?", "Superuser Stackexchange\n\nQ: What is the difference between chkdsk /f and chkdsk /r? \n/F              Fixes errors on the disk.\n/R              Locates bad sectors and recovers readable information (implies /F).\n\n(from dir /?)\nIt just sounds like the same thing to me.  I never know which one to use - is there even a difference?  Or is it maybe for backwards compatibility with previous versions of Windows where there was a difference?\n\nA: chkdsk /r does the same thing as chkdsk /f only it also checks for bad sectors on the disk and recovers any readable information. Running chkdsk /r implies that also chkdsk /f is run.\nchkdsk /f only checks for disk errors, not bad sectors.\nMicrosoft has a detailed page for chkdsk. The following is a snippet explaining /f and /r parameters.\n\nFixing disk errors:\nChkdsk corrects disk errors only if you specify the /f command-line\n  option. Chkdsk must be able to lock the drive to correct errors.\n  Because repairs usually change a disk's file allocation table and\n  sometimes cause a loss of data, chkdsk sends a confirmation message.\nFinding physical disk errors:\nUse the /r command-line option to find physical disk errors in the\n  file system.\n\n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1722439388.9707, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "10ed8e581f62414c872938cd8160de29", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": "who does peggy carter marry?", "0_output": [["who does peggy carter marry?", "Scifi Stackexchange\n\nQ: What is the name of Peggy's husband in Captain America: The Winter Soldier? I'm having a hard time hearing the name of Peggy's husband she names. I'm not the greatest at spelling but it sounds like his name is something like: Emanhu Winter?\nHere's the scene if anyone can help, he's named at 1:40:\n\n\n\n\nA: She doesn't give a specific name. The phrase she utters at 1:40 is:\n\nHe saved over 1000 men, including the man who, would ahh, who would become my husband, as it turned out\n\nwhich I guess you hear as Emanhu Winter, because of the slight hesitation and repetition there in the line delivery.\n"]], "0_corpus": "stackexchange", "1_conv_id": "512b56cafcf548dc8e1ba766787e9dee", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "who does peggy carter marry?", "1_output": [["who does peggy carter marry?", "Scifi Stackexchange\n\nQ: When did Captain America and Peggy Carter dance in the comics? Sometime this year, I read a comic book or graphic novel in which Steve Rogers (Captain America) returned from the ice, and gets to dance at last with an old Peggy Carter.\nDoes anyone know which comic that was? I can't find it anymore.\nThanks for your help!\n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1722439938.8404, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "d9c71637975b4303bfbcbad02f1bd062", "0_model_name": "voyage-multilingual-2", "0_prompt": "A paper that proposes an improved Transformer model capable of learning long-term dependencies beyond fixed-length context in language modeling.", "0_output": [["A paper that proposes an improved Transformer model capable of learning long-term dependencies beyond fixed-length context in language modeling.", "Title: Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context\n\nAbstract: Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning dependency beyond a fixed length without disrupting temporal coherence. It consists of a segment-level recurrence mechanism and a novel positional encoding scheme. Our method not only enables capturing longer-term dependency, but also resolves the context fragmentation problem. As a result, Transformer-XL learns dependency that is 80% longer than RNNs and 450% longer than vanilla Transformers, achieves better performance on both short and long sequences, and is up to 1,800+ times faster than vanilla Transformers during evaluation. Notably, we improve the state-of-the-art results of bpc/perplexity to 0.99 on enwiki8, 1.08 on text8, 18.3 on WikiText-103, 21.8 on One Billion Word, and 54.5 on Penn Treebank (without finetuning). When trained only on WikiText-103, Transformer-XL manages to generate reasonably coherent, novel text articles with thousands of tokens. Our code, pretrained models, and hyperparameters are available in both Tensorflow and PyTorch."]], "0_corpus": "arxiv", "1_conv_id": "1802d7d52d704f829a4f4992ff41fc69", "1_model_name": "text-embedding-3-large", "1_prompt": "A paper that proposes an improved Transformer model capable of learning long-term dependencies beyond fixed-length context in language modeling.", "1_output": [["A paper that proposes an improved Transformer model capable of learning long-term dependencies beyond fixed-length context in language modeling.", "Title: Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context\n\nAbstract: Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning dependency beyond a fixed length without disrupting temporal coherence. It consists of a segment-level recurrence mechanism and a novel positional encoding scheme. Our method not only enables capturing longer-term dependency, but also resolves the context fragmentation problem. As a result, Transformer-XL learns dependency that is 80% longer than RNNs and 450% longer than vanilla Transformers, achieves better performance on both short and long sequences, and is up to 1,800+ times faster than vanilla Transformers during evaluation. Notably, we improve the state-of-the-art results of bpc/perplexity to 0.99 on enwiki8, 1.08 on text8, 18.3 on WikiText-103, 21.8 on One Billion Word, and 54.5 on Penn Treebank (without finetuning). When trained only on WikiText-103, Transformer-XL manages to generate reasonably coherent, novel text articles with thousands of tokens. Our code, pretrained models, and hyperparameters are available in both Tensorflow and PyTorch."]], "1_corpus": "arxiv"}
-{"tstamp": 1722440194.3406, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "cef97f776ff242d6ad09eee2574b7864", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": "what does it mean if a car is a fleet vehicle", "0_output": [["what does it mean if a car is a fleet vehicle", "Vehicle registration plates of Indonesia\n\nBlack on yellow: Commercial vehicle or public transportation such as buses, taxis, angkot, auto rickshaws and commercial trucks.\nWhite on red: Used by fire departments, government ambulances, government officials and other governmental vehicles administered under their respective local governments.\nBlack on Red: Vehicles belonging to foreign countries. Commonly used by foreign embassies or vehicles belonging to International organizations.\nBlack on White with Black Trim: Vehicles belonging to diplomatic corps of foreign countries. Commonly used by foreign embassies or vehicles belonging to international organizations.\nBlack on green: Free Zone vehicles i.e. Batam (see Indonesia–Malaysia–Singapore Growth Triangle).\nBlue on white: Vehicles belonging to foreign countries, mainly used before the vehicle has been registered.\nElectric vehicles\nThe Indonesian National Police has set a special license plate for electric vehicles with additional blue trim at the expiry date row in accordance with the regulations in the Decree of the Head of the Traffic Corps of the Indonesian National Police in 2020.\nHowever, when personal fossil-fueled vehicle plates have changed the color scheme, personal EV plates are still using the old one (white on black) with blue trim, added with the implementation of FE-Schrift since November 2022.\nWhite on Black with Blue Trim: for privately owned electric vehicles and rental electric vehicles"]], "0_corpus": "wikipedia", "1_conv_id": "8fb7fcd108be4239a41e863c284cde1a", "1_model_name": "text-embedding-004", "1_prompt": "what does it mean if a car is a fleet vehicle", "1_output": [["what does it mean if a car is a fleet vehicle", "Fleet vehicle\n\nA fleet vehicle is a vehicle owned or leased by a business, government agency, or other organization rather than by an individual or family. Typical examples include vehicles operated by car rental companies, taxicab companies, public utilities, public transport, and emergency services.\nMany businesses purchase or lease fleet vehicles to deliver goods to customers, as well as providing vehicles for sales representatives to travel to clients. In some jurisdictions and countries, fleet vehicles can also be privately owned by employees. These vehicles are often called the \"grey fleet\" and are used for work purposes. Fleet vehicles can be managed by a fleet manager or transport manager using fleet management software. Vehicles may be connected to a fleet telematics system by way of a Fleet Management System, also known as an FMS.\nFederal Vehicle Fleet\nIn the United States, \"Federal Vehicle Fleet\" refers to the federal government's vehicles."]], "1_corpus": "wikipedia"}

data/retrieval_battle-cc11c9ee-fc93-4cd6-a4b5-246a7f21038c.jsonl

@@ -2,10 +2,8 @@
 {"tstamp": 1722317920.8917, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "c9ece7138b2c47438982e824f0959312", "0_model_name": "text-embedding-3-large", "0_prompt": "What's the smallest country in North America by landmass?", "0_output": [["What's the smallest country in North America by landmass?", "List of North American countries by area\n\nBelow is a list of countries and dependencies in North America by area. The region includes Canada, the Caribbean, Saint Pierre and Miquelon, Central America, Greenland, Mexico, and the United States. Canada is the largest country in North America and the Western Hemisphere. Saint Kitts and Nevis is the smallest country in North America overall, while El Salvador is the smallest country on the mainland. Greenland is a dependent territory of Denmark, but would be the third-largest country in the continent."]], "0_corpus": "wikipedia", "1_conv_id": "787cae92077a4584a9ae432ac2d99719", "1_model_name": "embed-english-v3.0", "1_prompt": "What's the smallest country in North America by landmass?", "1_output": [["What's the smallest country in North America by landmass?", "List of North American countries by area\n\nBelow is a list of countries and dependencies in North America by area. The region includes Canada, the Caribbean, Saint Pierre and Miquelon, Central America, Greenland, Mexico, and the United States. Canada is the largest country in North America and the Western Hemisphere. Saint Kitts and Nevis is the smallest country in North America overall, while El Salvador is the smallest country on the mainland. Greenland is a dependent territory of Denmark, but would be the third-largest country in the continent."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722318278.0368, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "6bdc0d5677eb44748bd3bdefff26096d", "0_model_name": "embed-english-v3.0", "0_prompt": "where was part of the classic surfing movie endless summer filmed", "0_output": [["where was part of the classic surfing movie endless summer filmed", "The Endless Summer\n\nDevelopment Brown remembered, \"I felt if I could take two years to make a film, maybe I could make something special\". To do this, he would need a bigger budget than he had on previous films. To raise the $50,000 budget for The Endless Summer, Brown took the best footage from his four previous films and made Waterlogged. With the money raised from Waterlogged, Brown filmed The Endless Summer, his sixth surf film.\nBrown's original concept was for Cape St. Francis to be the main destination, but through the suggestion of a travel agent, during the planning stages of the film, that a round-the-world ticket, would cost $50 cheaper than just a Los Angeles to Cape Town, South Africa round-trip flight. after which Bruce came up with the idea of following the summer season by traveling up and down the world.\nProduction\nThe Endless Summer was filmed in:\nSouthern Hemisphere: South Africa (Cape St. Francis), Australia, New Zealand (Manu Bay), and Tahiti.\nNorthern Hemisphere: Hawaii, California (Salt Creek Beach, Steamer Lane, Malibu), Senegal, Ghana (Labadi), and Nigeria.\nMike Hynson and Robert August had to pay $1,400 for their own around-the-world tickets, and Brown required a commitment of three months. Production lasted four months."]], "0_corpus": "wikipedia", "1_conv_id": "d31ab98a1c3b46a08bb6fb563b2df10f", "1_model_name": "voyage-multilingual-2", "1_prompt": "where was part of the classic surfing movie endless summer filmed", "1_output": [["where was part of the classic surfing movie endless summer filmed", "The Endless Summer\n\nThe Endless Summer is a 1966 American surf documentary film directed, produced, edited and narrated by Bruce Brown. The film follows surfers Mike Hynson and Robert August on a surfing trip around the world.  Despite the balmy mediterranean climate of their native California, cold ocean currents make local beaches inhospitable during the winter, without later, modern wetsuits. They travel to the coasts of Australia, New Zealand, Tahiti, Hawaii, Senegal (Dakar), Ghana, Nigeria and South Africa in a quest for new surf spots while introducing locals to the sport along the way."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722322699.2186, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "1caa8ef026264cbf92daec052861cdd0", "0_model_name": "text-embedding-004", "0_prompt": "Who is Napoleon?", "0_output": [["Who is Napoleon?", "Napoleon\n\nNapoleon Bonaparte (born Napoleone di Buonaparte; 15 August 1769 \u2013 5 May 1821), later known by his regnal name Napoleon\u00a0I, was a French military and political leader who rose to prominence during the French Revolution and led a series of successful campaigns across Europe during the Revolutionary Wars and Napoleonic Wars from 1796 to 1815. He was the leader of the French Republic as First Consul from 1799 to 1804, then of the French Empire as Emperor of the French from 1804 to 1814, and briefly again in 1815.\nBorn on the island of Corsica to a family of Italian origin, Napoleon moved to mainland France in 1779 and was commissioned as an officer in the French Army in 1785. He supported the French Revolution in 1789, and promoted its cause in Corsica. He rose rapidly in the ranks after breaking the siege of Toulon in 1793 and firing on royalist insurgents in Paris on 13 Vend\u00e9miaire in 1795. In 1796, Napoleon commanded a military campaign against the Austrians and their Italian allies in the War of the First Coalition, scoring decisive victories and becoming a national hero. He led an expedition to Egypt and Syria in 1798 which served as a springboard to political power. In November 1799, Napoleon engineered the Coup of 18 Brumaire against the Directory, and became First Consul of the Republic. He won the Battle of Marengo in 1800, which secured French victory in the War of the Second Coalition, and in 1803 sold the territory of Louisiana to the United States, which doubled the latter's area. In December 1804, Napoleon crowned himself Emperor of the French, further expanding his power."]], "0_corpus": "wikipedia", "1_conv_id": "7f6b33e7c9074fd4a1cae2f02ba4f4ed", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "Who is Napoleon?", "1_output": [["Who is Napoleon?", "Napoleon\n\nNapoleon Bonaparte (born Napoleone di Buonaparte; 15 August 1769 \u2013 5 May 1821), later known by his regnal name Napoleon\u00a0I, was a French military and political leader who rose to prominence during the French Revolution and led a series of successful campaigns across Europe during the Revolutionary Wars and Napoleonic Wars from 1796 to 1815. He was the leader of the French Republic as First Consul from 1799 to 1804, then of the French Empire as Emperor of the French from 1804 to 1814, and briefly again in 1815.\nBorn on the island of Corsica to a family of Italian origin, Napoleon moved to mainland France in 1779 and was commissioned as an officer in the French Army in 1785. He supported the French Revolution in 1789, and promoted its cause in Corsica. He rose rapidly in the ranks after breaking the siege of Toulon in 1793 and firing on royalist insurgents in Paris on 13 Vend\u00e9miaire in 1795. In 1796, Napoleon commanded a military campaign against the Austrians and their Italian allies in the War of the First Coalition, scoring decisive victories and becoming a national hero. He led an expedition to Egypt and Syria in 1798 which served as a springboard to political power. In November 1799, Napoleon engineered the Coup of 18 Brumaire against the Directory, and became First Consul of the Republic. He won the Battle of Marengo in 1800, which secured French victory in the War of the Second Coalition, and in 1803 sold the territory of Louisiana to the United States, which doubled the latter's area. In December 1804, Napoleon crowned himself Emperor of the French, further expanding his power."]], "1_corpus": "wikipedia"}
-{"tstamp": 1722322823.912, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "a8361e48a2134bafa6135be25d8ad87d", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": "What is Sentence Transformers?", "0_output": [["What is Sentence Transformers?", "BERT (language model)\n\nBidirectional Encoder Representations from Transformers (BERT) is a language model based on the transformer architecture, notable for its dramatic improvement over previous state of the art models. It was introduced in October 2018 by researchers at Google. A 2020 literature survey concluded that \"in a little over a year, BERT has become a ubiquitous baseline in Natural Language Processing (NLP) experiments counting over 150 research publications analyzing and improving the model.\"\nBERT was originally implemented in the English language at two model sizes: (1) BERTBASE: 12 encoders with 12 bidirectional self-attention heads totaling 110 million parameters, and (2) BERTLARGE: 24 encoders with 16 bidirectional self-attention heads totaling 340 million parameters. Both models were pre-trained on the Toronto BookCorpus (800M words) and English Wikipedia  (2,500M words).\nDesign\nBERT is an \"encoder-only\" transformer architecture."]], "0_corpus": "wikipedia", "1_conv_id": "a162df51a68a4151bf8a4c558811e179", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": "What is Sentence Transformers?", "1_output": [["What is Sentence Transformers?", "Transformers\n\nIn 1986, the cartoon became the film The Transformers: The Movie, which is set in the year 2005. It introduced the Matrix as the \"Autobot Matrix of Leadership\", as a fatally wounded Prime gives it to Ultra Magnus; however, as Prime dies he drops the matrix, which is then caught by Hot Rod who subsequently becomes Rodimus Prime later on in the film. Unicron, a Transformer who devours planets, fears its power and re-creates a heavily damaged Megatron as Galvatron, as well as Bombshell or Skywarp becoming Cyclonus, Thundercracker becoming Scourge and two other Insecticons becoming Scourge's huntsmen, the Sweeps. Eventually, Rodimus Prime takes out the Matrix and destroys Unicron. In the United Kingdom, the weekly comic book interspliced original material to keep up with U.S. reprints, and The Movie provided much new material. Writer Simon Furman proceeded to expand the continuity with movie spin-offs involving the time travelling Galvatron. The Movie also featured guest voices from Leonard Nimoy as Galvatron, Scatman Crothers as Jazz, Casey Kasem as Cliffjumper, Orson Welles as Unicron and Eric Idle as the leader of the Junkions (Wreck-Gar, though unnamed in the movie). The Transformers theme tune for the film was performed by Lion with \"Weird Al\" Yankovic adding a song to the soundtrack."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722322871.5465, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "7f876a2cc06144d8bccbdef351f800f2", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "Chicaco attacks", "0_output": [["Chicaco attacks", "USS Chicago (CA-29)\n\nWorld War II When the Japanese attacked Pearl Harbor on 7 December 1941, Chicago was at sea with Task Force 12 and the Force immediately began a five-day sweep in the Oahu-Johnston-Palmyra triangle in an effort to intercept the enemy. The Force returned to Pearl Harbor on 12 December; from 14\u201327 December, Chicago operated with Task Force 11 on patrol and search missions.\nOn 2 February 1942, Chicago departed Pearl Harbor for Suva, where she joined the newly formed ANZAC Squadron, later redesignated as Task Force 44. During March and April, the cruiser operated off the Louisiade Archipelago, covering the attacks on Lae and Salamaua, New Guinea. In a position to intercept enemy surface units which attempted to attack Port Moresby, Chicago also provided cover for the arrival of American troops on New Caledonia.\nOn 1 May, Chicago was ordered from Noum\u00e9a to join Commander, Southwest Pacific, and on the 4th she supported  in her strike against the Japanese on Tulagi, Solomon Islands during the Battle of the Coral Sea. On 7 May, she proceeded, with the Support Group, to intercept and attack the Japanese Port Moresby invasion group. The following day, the group underwent several Japanese air attacks, during which Chicago suffered several casualties from strafing, but drove off the planes and proceeded ahead until it was clear that the Japanese force had been turned back."]], "0_corpus": "wikipedia", "1_conv_id": "39e24b7acfbc433fbe17b9aaebd9b23b", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "Chicaco attacks", "1_output": [["Chicaco attacks", "Chiapas conflict\n\n6 July 2003: Violent acts take place during the legislative elections in indigenous regions of Chiapas, principally in San Juan Cancuc, Zinacant\u00e1n and Chenalh\u00f3. At the federal level, the largest rate of absenteeism was registered in the recent history of the country.\nSeptember/October 2003: A series of conflicts between members of the Independent Center of Agricultural Workers and Campesinos (CIOAC) and Zapatistas, around the detention of Arm\u00edn Morales Jim\u00e9nez by militants of the EZLN for the accused crime of abuse of confidence.\n22 January 2004: The houses of the community of Nuevo San Rafael in Montes Azules Reserve were all burned. According to the Secretary of Agrarian Reform (SRA), the inhabitants had voluntarily decided to abandon their homes and return to their places of origin. NGOs accused the SRA of having divided the population so as to force residents to leave the reserve.\n10 April 2004: Zapatista supporters from the municipality of Zinacant\u00e1n were ambushed by members of the PRD, leaving dozens wounded and displacing 125 Zapatista families.\n23 April 2004: Noel P\u00e1vel Gonz\u00e1lez, student at the National Autonomous University of Mexico and the National School of Anthropology and History, was found murdered in Mexico City. Elements of the investigation point towards the involvement of the ultra-right group \"El Yunque.\""]], "1_corpus": "wikipedia"}
 {"tstamp": 1722322910.9879, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "830fdba8c1114ad692ade33324a03c62", "0_model_name": "BM25", "0_prompt": "What is Hugging Face?", "0_output": [["What is Hugging Face?", "X-23\n\nAfter being found by Kitty, Laura was taken to their base to recover. When Laura wakes up, with her memories now returning, at the former Weapon X facility she immediately tries to escape. Teen Cyclops goes to talk to her, while he's gone Teen Jean mentions to the team that Teen Cyclops \"fancies\" Laura. As Laura makes it outside she's met by Teen Cyclops, who was waiting for her. Teen Cyclops tries to calm her down but Laura only ceases hostility after getting Teen Cyclops scent and figuring out that Teen Cyclops is who he says he is.\nThe two talk, with Laura telling him that she does not want to talk about her past or what happened to her. Suddenly Teen Cyclops awkwardly hugs her, when she was distracted and looking sad, because he thought she needed a hug. Laura tells him that she does not do hugs, especially not with an audience as Kitty, Teen Jean, and Teen Beast were watching them. Teen Jean expressed a very conflicted look upon her face due to the hug and having read part of Laura's mind, thus she knows what happened in Avengers Arena. Laura thanks them for helping her and suggests they take the fight to the Purifiers, but in order for her to track them down, they will have to return her to where they found her."]], "0_corpus": "wikipedia", "1_conv_id": "395cfbc72eed40159ded5a02938adc32", "1_model_name": "embed-english-v3.0", "1_prompt": "What is Hugging Face?", "1_output": [["What is Hugging Face?", "Hug\n\nEtymology The origins of the word are unknown, but two hypotheses exist. The first is that the verb \"hug\" (first used in the 1560s) could be related to the Old Norse word , which meant to comfort. The second hypothesis is that the word is related to the German word , which means to foster or cherish, and originally meant to enclose with a hedge.\nCharacteristics\nA hug, sometimes in association with a kiss, is a form of nonverbal communication. Depending on culture, context and relationship, a hug can indicate familiarity, love, affection, friendship, brotherhood, flirting or sympathy.\nHugs can indicate support, comfort, and consolation, particularly where words are insufficient. A hug usually demonstrates affection and emotional warmth, sometimes arising from joy or happiness when reunited with someone or seeing someone absent after a long time. A non-reciprocal hug may demonstrate a relational problem.\nA hug can range from a brief one-second squeeze, with the arms not fully around the other person, to an extended holding. The length of a hug in any situation is socially and culturally determined. In the case of lovers, and occasionally others, the hips may also be pressed together. The emotionality of the hug can also influence the direction of the hug.\nThe direction of hugs generally corresponds with handedness, with right-handed people tending to lead with the right hand, but a heightened emotional state makes it slightly more likely for people to lead with the left hand. This small but significant effect has been attributed to right-hemispheric emotional processing."]], "1_corpus": "wikipedia"}
-{"tstamp": 1722322936.8317, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "49aa9d1fb4e54f14b66643bdec0e311a", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": "What is NLTK?", "0_output": [["What is NLTK?", "Computational linguistics\n\nComputational linguistics is an interdisciplinary field concerned with the computational modelling of natural language, as well as the study of appropriate computational approaches to linguistic questions. In general, computational linguistics draws upon linguistics, computer science, artificial intelligence, mathematics, logic, philosophy, cognitive science, cognitive psychology, psycholinguistics, anthropology and neuroscience, among others.\nOrigins\nThe field overlapped with artificial intelligence since the efforts in the United States in the 1950s to use computers to automatically translate texts from foreign languages, particularly Russian scientific journals, into English. Since rule-based approaches were able to make arithmetic (systematic) calculations much faster and more accurately than humans, it was expected that lexicon, morphology, syntax and semantics can be learned using explicit rules, as well. After the failure of rule-based approaches, David Hays coined the term in order to distinguish the field from AI and co-founded both the Association for Computational Linguistics (ACL) and the International Committee on Computational Linguistics (ICCL) in the 1970s and 1980s. What started as an effort to translate between languages evolved into a much wider field of natural language processing.\nAnnotated corpora\nIn order to be able to meticulously study the English language, an annotated text corpus was much  needed. The Penn Treebank was one of the most used corpora. It consisted of IBM computer manuals, transcribed telephone conversations, and other texts, together containing over 4.5 million words of American English, annotated using both  part-of-speech tagging and syntactic bracketing."]], "0_corpus": "wikipedia", "1_conv_id": "340198e89e784b64b1d79d0e65500135", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "What is NLTK?", "1_output": [["What is NLTK?", "Natural Language Toolkit\n\nThe Natural Language Toolkit, or more commonly NLTK, is a suite of libraries and programs for symbolic and statistical natural language processing (NLP) for English written in the Python programming language. It supports classification, tokenization, stemming, tagging, parsing, and semantic reasoning functionalities. It was developed by Steven Bird and Edward Loper in the Department of Computer and Information Science at the University of Pennsylvania. NLTK includes graphical demonstrations and sample data. It is accompanied by a book that explains the underlying concepts behind the language processing tasks supported by the toolkit, plus a cookbook.\nNLTK is intended to support research and teaching in NLP or closely related areas, including empirical linguistics, cognitive science, artificial intelligence, information retrieval, and machine learning.\nNLTK has been used successfully as a teaching tool, as an individual study tool, and as a platform for prototyping and building research systems. There are 32 universities in the US and 25 countries using NLTK in their courses.\nLibrary highlights\nDiscourse representation\nLexical analysis: Word and text tokenizer\nn-gram and collocations\nPart-of-speech tagger\nTree model and Text chunker for capturing"]], "1_corpus": "wikipedia"}
 {"tstamp": 1722323001.851, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "904708d39d7f40d69ba876b5f826d941", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "What is the tallest peak in Warren Range, Antarctica?", "0_output": [["What is the tallest peak in Warren Range, Antarctica?", "Vinson Massif\n\nMount Vinson is the highest peak in Antarctica, at . It lies in the north part of Vinson Massif's summit plateau in the south portion of the main ridge of the Sentinel Range about  north of Hollister Peak. It was first climbed in 1966 by an American team led by Nicholas Clinch. An expedition in 2001 was the first to climb via the Eastern route, and also took GPS measurements of the height of the peak. As of February 2010, 1,400 climbers have attempted to reach the summit of Mount Vinson. Mount Vinson is ranked 6th by topographic isolation.\nGeography\nThe Vinson Massif extends between Goodge Col and Branscomb Glacier to the northwest, Nimitz Glacier and Gildea Glacier to the southwest and south, and Dater Glacier and its tributary Hinkley Glacier to the east. The southeastern part of the massif ends at Hammer Col, which joins it to the Craddock Massif, of which the highest point is Mount Rutford (). The massif comprises both the high central Vinson Plateau with its few peaks rising to over , and several side ridges mostly trending southwest or northeast from the plateau.\nThe current height () of Mount Vinson was measured by a GPS survey that was conducted by the 2004 Omega Foundation team comprising Damien Gildea of Australia (leader) and Rodrigo Fica and Camilo Rada of Chile. Since 1998 and continuing through 2007, the Omega Foundation has placed a GPS receiver on the summit for a suitable period of time to obtain accurate satellite readings."]], "0_corpus": "wikipedia", "1_conv_id": "a21aee4127f6494db3edb0f70eebdbc1", "1_model_name": "intfloat/multilingual-e5-large-instruct", "1_prompt": "What is the tallest peak in Warren Range, Antarctica?", "1_output": [["What is the tallest peak in Warren Range, Antarctica?", "Vinson Massif\n\nMount Vinson is the highest peak in Antarctica, at . It lies in the north part of Vinson Massif's summit plateau in the south portion of the main ridge of the Sentinel Range about  north of Hollister Peak. It was first climbed in 1966 by an American team led by Nicholas Clinch. An expedition in 2001 was the first to climb via the Eastern route, and also took GPS measurements of the height of the peak. As of February 2010, 1,400 climbers have attempted to reach the summit of Mount Vinson. Mount Vinson is ranked 6th by topographic isolation.\nGeography\nThe Vinson Massif extends between Goodge Col and Branscomb Glacier to the northwest, Nimitz Glacier and Gildea Glacier to the southwest and south, and Dater Glacier and its tributary Hinkley Glacier to the east. The southeastern part of the massif ends at Hammer Col, which joins it to the Craddock Massif, of which the highest point is Mount Rutford (). The massif comprises both the high central Vinson Plateau with its few peaks rising to over , and several side ridges mostly trending southwest or northeast from the plateau.\nThe current height () of Mount Vinson was measured by a GPS survey that was conducted by the 2004 Omega Foundation team comprising Damien Gildea of Australia (leader) and Rodrigo Fica and Camilo Rada of Chile. Since 1998 and continuing through 2007, the Omega Foundation has placed a GPS receiver on the summit for a suitable period of time to obtain accurate satellite readings."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722323230.7402, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "85a1e2d08d814c8dbb96e7f1b320b523", "0_model_name": "Salesforce/SFR-Embedding-2_R", "0_prompt": "Who did Cleopatra have a baby with?", "0_output": [["Who did Cleopatra have a baby with?", "Cleopatra Selene II\n\nCleopatra Selene II (Greek: \u039a\u03bb\u03b5\u03bf\u03c0\u03ac\u03c4\u03c1\u03b1 \u03a3\u03b5\u03bb\u03ae\u03bd\u03b7; summer 40 BC \u2013  BC; the numeration is modern) was a Ptolemaic princess, Queen of Numidia (briefly in 25 BC) and Mauretania (25 BC \u2013 5 BC) and Queen of Cyrenaica (34 BC \u2013 30 BC). She was an important royal woman in the early Augustan age.\nCleopatra Selene was the only daughter of Greek Ptolemaic Queen Cleopatra VII of Egypt and Roman Triumvir Mark Antony. In the Donations of Antioch and of Alexandria, she was made queen of Cyrenaica and Libya. After Antony and Cleopatra's defeat at Actium and their suicides in Egypt in 30 BC, Selene and her brothers were brought to Rome and placed in the household of Octavian's sister, Octavia the Younger, a former wife of her father.\nSelene married Juba II of Numidia and Mauretania. She had great influence in Mauretania's government decisions, especially regarding trade and construction projects. During their reign, the country became extremely wealthy. The couple had a son and successor, Ptolemy of Mauretania. Through their granddaughter Drusilla, the Ptolemaic line intermarried into Roman nobility for many generations.\nEarly life"]], "0_corpus": "wikipedia", "1_conv_id": "f0684e70908c49fbbd04e82653771ae6", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_prompt": "Who did Cleopatra have a baby with?", "1_output": [["Who did Cleopatra have a baby with?", "Caesarion\n\nPtolemy XV Caesar (; , ; 23 June 47 BC \u2013 29 August 30 BC), nicknamed Caesarion (, , \"Little Caesar\"), was the last pharaoh of Ptolemaic Egypt, reigning with his mother Cleopatra VII from 2 September 44 BC until her death by 12 August 30 BC, then as sole ruler until his death was ordered by Octavian (who would become the first Roman emperor as Augustus).\nCaesarion was the eldest son of Cleopatra and the only known biological son of Julius Caesar, after whom he was named. He was the last sovereign member of the Ptolemaic dynasty of Egypt."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722323274.8692, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "00d652f1a43645d78978d938ceec2a54", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": "What is Twitter?", "0_output": [["What is Twitter?", "Twitter\n\nX, commonly referred to by its former name Twitter, is a social networking service. It is one of the world's largest social media websites and the seventh-most visited website in the world. Users can share short text messages, images, and videos in posts (formerly \"tweets\") and like or repost/retweet other users' content. X also includes direct messaging, video and audio calling, bookmarks, lists and communities, and Spaces, a social audio feature. Users can vote on context added by approved users using the Community Notes feature.\nTwitter was created in March 2006 by Jack Dorsey, Noah Glass, Biz Stone, and Evan Williams, and was launched in July of that year. Twitter grew quickly; by 2012 more than 100 million users produced 340 million tweets per day. Twitter, Inc., was based in San Francisco, California, and had more than 25 offices around the world. A signature characteristic of the service is that posts are required to be brief. Posts were initially limited to 140 characters, which was changed to 280 characters in 2017 and removed for paid accounts in 2023. The majority of tweets are produced by a minority of users. In 2020, it was estimated that approximately 48 million accounts (15 percent of all accounts) were not genuine people."]], "0_corpus": "wikipedia", "1_conv_id": "04719f7eea0f496692e75d85656d0b47", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "What is Twitter?", "1_output": [["What is Twitter?", "Twitter\n\nX, commonly referred to by its former name Twitter, is a social networking service. It is one of the world's largest social media websites and the seventh-most visited website in the world. Users can share short text messages, images, and videos in posts (formerly \"tweets\") and like or repost/retweet other users' content. X also includes direct messaging, video and audio calling, bookmarks, lists and communities, and Spaces, a social audio feature. Users can vote on context added by approved users using the Community Notes feature.\nTwitter was created in March 2006 by Jack Dorsey, Noah Glass, Biz Stone, and Evan Williams, and was launched in July of that year. Twitter grew quickly; by 2012 more than 100 million users produced 340 million tweets per day. Twitter, Inc., was based in San Francisco, California, and had more than 25 offices around the world. A signature characteristic of the service is that posts are required to be brief. Posts were initially limited to 140 characters, which was changed to 280 characters in 2017 and removed for paid accounts in 2023. The majority of tweets are produced by a minority of users. In 2020, it was estimated that approximately 48 million accounts (15 percent of all accounts) were not genuine people."]], "1_corpus": "wikipedia"}
@@ -24,4 +22,3 @@
 {"tstamp": 1722330827.7238, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "5e344846ff64426d805b30702aef5bb5", "0_model_name": "embed-english-v3.0", "0_prompt": "Who is the youngest spaniard to win a grand slam?", "0_output": [["Who is the youngest spaniard to win a grand slam?", "Tennis in Spain\n\nRafael Nadal is regarded as the greatest Spanish player of all time. He has won 22 Grand Slam men's singles titles, the second-most in tennis history. He has won the French Open a record 14 times, between 2005 and 2022. After defeating then-world No. 1 Roger Federer in 2008, Nadal claimed the Wimbledon title in a historic final, having won the tournament twice thus far. In 2009, he became the first Spaniard to win the Australian Open, a feat he repeated at the 2022 Australian Open. After defeating Novak Djokovic in the 2010 US Open final, he became the first man in history to win majors on clay, grass, and hard courts in a calendar year (Surface Slam), and the first Spaniard to complete a Career Grand Slam, which he has achieved twice in singles (one of four men in history to do so). In addition, Nadal is one of two men to achieve the Career Golden Slam in singles and a two-time Olympic gold medalist, winning the singles event at the 2008 Beijing Olympics and the doubles event at the 2016 Rio Olympics.\nSpain has produced several other world No. 1 players; Arantxa S\u00e1nchez Vicario (a 3-time French Open champion in 1989, 1994 and 1998, and 1994 US Open champion), Carlos Moy\u00e1 (1998 French Open champion), Juan Carlos Ferrero (2003 French Open champion), Garbi\u00f1e Muguruza (2016 French Open and 2017 Wimbledon champion) and Carlos Alcaraz (2022 US Open and 2023 Wimbledon champion).\nOther Grand Slam champions are Manuel Santana (1961 and 1964 French Open, 1966 Wimbledon and 1965 US Open champion), Sergi Bruguera (1993 and 1994 French Open champion), Andr\u00e9s Gimeno (1972 French Open champion), Manuel Orantes (1975 US Open champion), Conchita Mart\u00ednez (1994 Wimbledon champion), and Albert Costa (2002 French Open champion).\nTournaments held in Spain on the men's tour every year include the Madrid Masters, Barcelona, and Mallorca. Madrid Masters is also contested by women in the WTA Tour.\nList of Spanish tennis players (Open Era only)\nOnly includes players ranked in the top 50. Bold names indicate currently active players.\nMen\nWomen\nGrand Slam performances of Spanish tennis players\nOnly includes players who have reached at least a Grand Slam quarterfinal"]], "0_corpus": "wikipedia", "1_conv_id": "8792c6fdadff4227bf787fecd15756c8", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "Who is the youngest spaniard to win a grand slam?", "1_output": [["Who is the youngest spaniard to win a grand slam?", "Rebeka Masarova\n\nRebeka Masarova (, ; born 6 August 1999) is a Swiss-born Spanish professional tennis player. She has career-high WTA rankings of 62 in singles and 125 in doubles.\nThe 2016 French Open junior champion started representing Spain in January 2018.\nPersonal life\nMasarova's mother is Spanish and her father is Slovak. Born in Basel, hometown of Roger Federer, she was inspired to start playing tennis from watching Federer play in his first Wimbledon final in 2003.\nJunior career\nGrand Slam performance\nSingles:\nAustralian Open: F (2017)\nFrench Open: W (2016)\nWimbledon: 3R (2016)"]], "1_corpus": "wikipedia"}
 {"tstamp": 1722330966.5406, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "35d44656ae304532aa9f1a3885b87cee", "0_model_name": "text-embedding-004", "0_prompt": "What video game genre is based on platforming, new item/skill unlocks allowing new mobility and backtracking", "0_output": [["What video game genre is based on platforming, new item/skill unlocks allowing new mobility and backtracking", "Platformer\n\nA platformer (also called a platform game, and sometimes a jump 'n' run game) is a sub-genre of action video games in which the core objective is to move the player character between points in an environment. Platform games are characterized by levels with uneven terrain and suspended platforms of varying height that require jumping and climbing to traverse. Other acrobatic maneuvers may factor into the gameplay, such as swinging from vines or grappling hooks, jumping off walls, gliding through the air, or bouncing from springboards or trampolines."]], "0_corpus": "wikipedia", "1_conv_id": "f59e38ca09de43bfaab0c48326ab5253", "1_model_name": "GritLM/GritLM-7B", "1_prompt": "What video game genre is based on platforming, new item/skill unlocks allowing new mobility and backtracking", "1_output": [["What video game genre is based on platforming, new item/skill unlocks allowing new mobility and backtracking", "Metroidvania\n\nGameplay concepts The term 'Metroidvania' is most often used to refer to a platforming game that features a single large, interconnected map, generally with discrete rooms or sections. Not all areas of this map are available at the start, often requiring the player to obtain an item (such as a weapon or key) or a new character ability to remove some obstacle blocking the path forward. Often, this item is protected by a boss character, providing story-driven challenges throughout the game. Maps are non-linear, and often require the player to traverse the map multiple times during the course of the game. Weaker monsters will inhabit other parts of the level, re-spawning when the player revisits those rooms, and often can be defeated to gain health, ammunition, or experience points.\nLarger games generally feature save points as well as the ability to transport the player quickly between certain rooms on far sides of the map, eliminating tedious backtracking in the later parts of the game. Access to new abilities can also open up shortcuts that reduce travel time, as well as discover secrets that help to improve the character's abilities. For example, gaining access to double jump or wall jump abilities can give players more mobility, while obtaining the ability to transform into a smaller object can let the player slip through narrow corridors. As such, the genre focuses on exploration of a large world map, and advancement of the player-character abilities over time. Metroidvanias are sometimes referred to as \"platform adventure games\" due to this scope."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722332151.9612, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "b76d7f83119c4eff9849041c5f557883", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "is dark chocolate bittersweet or semisweet?", "0_output": [["is dark chocolate bittersweet or semisweet?", "Stackoverflow Stackexchange\n\nQ: what does \"free\" in context-free languages conceptually mean? \nPossible Duplicate:\nWhat is a Context Free Grammar? \n\nI can not understand the conceptual meaning of \"free\" in context free grammar or language? what is free?\n\nA: Context-free is in contrast to context-sensitive. While a context-sensitive grammar has rules of the form\n\u03b1A\u03b2 \u2192 \u03b1\u03b3\u03b2\n\na context-free one has rules conforming to\nA \u2192 \u03b3\n\ni.e. A is derivable to \u03b3 independent (free) of the context of \u03b1 and \u03b2.\nThe Wikipedia article on the Chomsky hierarchy explains it in more detail.\n"]], "0_corpus": "stackexchange", "1_conv_id": "10daca11329744e0bd10a3fd5573fbc9", "1_model_name": "GritLM/GritLM-7B", "1_prompt": "is dark chocolate bittersweet or semisweet?", "1_output": [["is dark chocolate bittersweet or semisweet?", "English Stackexchange\n\nQ: Must cookies contain chocolate in BrE? In British English, my friend informed me that my use of the word cookie was incorrect in referring to a baked item having no chocolate bits in it. Instead the appropriate term would have to be biscuit, as cookie always required some form of chocolate to be inside the biscuit.\nIs this assertion correct?\nThe Oxford dictionary only explains the meaning in American English, not British English: \"A sweet biscuit.\"\nIn case this is relevant, the object in question was a slightly sweet ginger-biscuit/cookie.\n\nA: Until recently, cookie was a foreign word in British English: because of American TV and films, most people knew that it was American for biscuit. \nCookie is now familiar for the large, sweet, not-very-crunchy confections sold in shopping malls. They come in various flavours, and don't have to have chocolate. \nI don't think anybody I know would call traditional-style English biscuits (whether Bath Oliver, Malted Milk, Garibaldi, Ginger nut, Fig roll, or even Hobnob) \"cookies\".\n"]], "1_corpus": "stackexchange"}
-{"tstamp": 1722332184.4765, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "c8aaeaa6441c4c09a1bb0388c01bc80d", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "the names of the beaches on d day", "0_output": [["the names of the beaches on d day", "List of beaches\n\nThis is a list of beaches of the world, sorted by country. A beach is a landform along the shoreline of an ocean, sea, lake, or river. It usually consists of loose particles, which are often composed of rock, such as sand, gravel, shingle, pebbles, or cobblestones. Beaches typically occur in areas along the coast where wave or current action deposits and reworks sediments. The particles comprising a beach are occasionally biological in origin, such as mollusc shells or coralline algae.\nAfghanistan\nQargha Reservoir\nAlbania\nAlgeria\nBeni Haoua Beach\nAngola\nBa\u00eda Azul\nCoatinha beach in Benguela\nAntigua and Barbuda\nThere are 365 beaches on Antigua.  The following are some of beaches in Antigua and Barbuda:\nCocoa Point beach, Barbuda,\nDevil's Bridge beach, Saint Philip, Antigua and Barbuda,\nEden beach, near Five Islands, Antigua,\nFive Islands beach, Antigua"]], "0_corpus": "wikipedia", "1_conv_id": "ca9942dc032840418985bc869ce4ec9f", "1_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "1_prompt": "the names of the beaches on d day", "1_output": [["the names of the beaches on d day", "Military engineering vehicle\n\n1918-1939 Between the wars various experimental bridging tanks were used to test a series of methods for bridging obstacles and developed by the Experimental Bridging Establishment (EBE). Captain SG Galpin RE conceived a prototype Light Tank Mk V to test the Scissors Assault Bridge. This concept was realised by Captain SA Stewart RE with significant input from a Mr DM Delany, a scientific civil servant in the employ of the EBE. MB Wild & Co, Birmingham, also developed a bridge that could span gaps of 26 feet using a complex system of steel wire ropes and a traveling jib, where the front section was projected and then attached to the rear section prior to launching the bridge. This system had to be abandoned due to lack of success in getting it to work, however the idea was later used successfully on the Beaver Bridge Laying Tank.\nEarly World War Two\nOnce World War Two had begun, the development of armoured vehicles for use by engineers in the field was accelerated under Delaney's direction. The EBE rapidly developed an assault bridge carried on a modified Covenanter tank capable of deploying a 24-ton tracked load capacity bridge (Class 24) that could span gaps of 30 feet. However, it did not see service in the British armed forces, and all vehicles were passed onto Allied forces such as Australia and Czechoslovakia.\nA Class 30 design superseded the Class 24 with no real re-design, simply the substitution of the Covenanter tank with a suitably modified Valentine.\nAs tanks in the war got heavier, a new bridge capable of supporting them was developed. A heavily modified Churchill used a single-piece bridge mounted on a turret-less tank and was able to lay the bridge in 90 seconds; this bridge was able to carry a 60-ton tracked or 40-ton wheeled load.\nLate World War 2: Hobart's 'Funnies' and D-Day"]], "1_corpus": "wikipedia"}

data/retrieval_battle-d39d676b-bc57-4c8b-9e61-c980be2f2db2.jsonl

@@ -2,15 +2,12 @@
 {"tstamp": 1722523256.729, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "bb3930e45d654dd3a2416d11721ecc43", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": "Improving state-of-the-art theorem proving with online training and HTPS algorithm", "0_output": [["Improving state-of-the-art theorem proving with online training and HTPS algorithm", "Title: HyperTree Proof Search for Neural Theorem Proving\n\nAbstract: We propose an online training procedure for a transformer-based automated theorem prover. Our approach leverages a new search algorithm, HyperTree Proof Search (HTPS), inspired by the recent success of AlphaZero. Our model learns from previous proof searches through online training, allowing it to generalize to domains far from the training distribution. We report detailed ablations of our pipeline's main components by studying performance on three environments of increasing complexity. In particular, we show that with HTPS alone, a model trained on annotated proofs manages to prove 65.4% of a held-out set of Metamath theorems, significantly outperforming the previous state of the art of 56.5% by GPT-f. Online training on these unproved theorems increases accuracy to 82.6%. With a similar computational budget, we improve the state of the art on the Lean-based miniF2F-curriculum dataset from 31% to 42% proving accuracy."]], "0_corpus": "arxiv", "1_conv_id": "aa8e8261cd8847c39c0154edb167a3ca", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "Improving state-of-the-art theorem proving with online training and HTPS algorithm", "1_output": [["Improving state-of-the-art theorem proving with online training and HTPS algorithm", "Title: HyperTree Proof Search for Neural Theorem Proving\n\nAbstract: We propose an online training procedure for a transformer-based automated theorem prover. Our approach leverages a new search algorithm, HyperTree Proof Search (HTPS), inspired by the recent success of AlphaZero. Our model learns from previous proof searches through online training, allowing it to generalize to domains far from the training distribution. We report detailed ablations of our pipeline's main components by studying performance on three environments of increasing complexity. In particular, we show that with HTPS alone, a model trained on annotated proofs manages to prove 65.4% of a held-out set of Metamath theorems, significantly outperforming the previous state of the art of 56.5% by GPT-f. Online training on these unproved theorems increases accuracy to 82.6%. With a similar computational budget, we improve the state of the art on the Lean-based miniF2F-curriculum dataset from 31% to 42% proving accuracy."]], "1_corpus": "arxiv"}
 {"tstamp": 1722523292.9067, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "44095baafff94dbab18b25562cd53dc4", "0_model_name": "voyage-multilingual-2", "0_prompt": "Paper introducing a large language model that enhances scientific research by reasoning and combining knowledge from scientific literature.", "0_output": [["Paper introducing a large language model that enhances scientific research by reasoning and combining knowledge from scientific literature.", "Title: Large Language Models for Scientific Synthesis, Inference and Explanation\n\nAbstract: Large language models are a form of artificial intelligence systems whose primary knowledge consists of the statistical patterns, semantic relationships, and syntactical structures of language1. Despite their limited forms of \"knowledge\", these systems are adept at numerous complex tasks including creative writing, storytelling, translation, question-answering, summarization, and computer code generation. However, they have yet to demonstrate advanced applications in natural science. Here we show how large language models can perform scientific synthesis, inference, and explanation. We present a method for using general-purpose large language models to make inferences from scientific datasets of the form usually associated with special-purpose machine learning algorithms. We show that the large language model can augment this \"knowledge\" by synthesizing from the scientific literature. When a conventional machine learning system is augmented with this synthesized and inferred knowledge it can outperform the current state of the art across a range of benchmark tasks for predicting molecular properties. This approach has the further advantage that the large language model can explain the machine learning system's predictions. We anticipate that our framework will open new avenues for AI to accelerate the pace of scientific discovery."]], "0_corpus": "arxiv", "1_conv_id": "e09bcd40896b4e23bc33768ff92a6e2a", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "Paper introducing a large language model that enhances scientific research by reasoning and combining knowledge from scientific literature.", "1_output": [["Paper introducing a large language model that enhances scientific research by reasoning and combining knowledge from scientific literature.", "Title: Galactica: A Large Language Model for Science\n\nAbstract: Information overload is a major obstacle to scientific progress. The explosive growth in scientific literature and data has made it ever harder to discover useful insights in a large mass of information. Today scientific knowledge is accessed through search engines, but they are unable to organize scientific knowledge alone. In this paper we introduce Galactica: a large language model that can store, combine and reason about scientific knowledge. We train on a large scientific corpus of papers, reference material, knowledge bases and many other sources. We outperform existing models on a range of scientific tasks. On technical knowledge probes such as LaTeX equations, Galactica outperforms the latest GPT-3 by 68.2% versus 49.0%. Galactica also performs well on reasoning, outperforming Chinchilla on mathematical MMLU by 41.3% to 35.7%, and PaLM 540B on MATH with a score of 20.4% versus 8.8%. It also sets a new state-of-the-art on downstream tasks such as PubMedQA and MedMCQA dev of 77.6% and 52.9%. And despite not being trained on a general corpus, Galactica outperforms BLOOM and OPT-175B on BIG-bench. We believe these results demonstrate the potential for language models as a new interface for science. We open source the model for the benefit of the scientific community."]], "1_corpus": "arxiv"}
 {"tstamp": 1722523305.7359, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "226290d7b5e143bd937280431432089f", "0_model_name": "text-embedding-004", "0_prompt": "Which paper discusses using Knowledge Distillation to make deep neural networks interpretable?", "0_output": [["Which paper discusses using Knowledge Distillation to make deep neural networks interpretable?", "Title: Improving the Interpretability of Deep Neural Networks with Knowledge Distillation\n\nAbstract: Deep Neural Networks have achieved huge success at a wide spectrum of applications from language modeling, computer vision to speech recognition. However, nowadays, good performance alone is not sufficient to satisfy the needs of practical deployment where interpretability is demanded for cases involving ethics and mission critical applications. The complex models of Deep Neural Networks make it hard to understand and reason the predictions, which hinders its further progress. To tackle this problem, we apply the Knowledge Distillation technique to distill Deep Neural Networks into decision trees in order to attain good performance and interpretability simultaneously. We formulate the problem at hand as a multi-output regression problem and the experiments demonstrate that the student model achieves significantly better accuracy performance (about 1\\% to 5\\%) than vanilla decision trees at the same level of tree depth. The experiments are implemented on the TensorFlow platform to make it scalable to big datasets. To the best of our knowledge, we are the first to distill Deep Neural Networks into vanilla decision trees on multi-class datasets."]], "0_corpus": "arxiv", "1_conv_id": "96d8179a1b0749f8a97f74777838eba5", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "Which paper discusses using Knowledge Distillation to make deep neural networks interpretable?", "1_output": [["Which paper discusses using Knowledge Distillation to make deep neural networks interpretable?", "Title: Improving the Interpretability of Deep Neural Networks with Knowledge Distillation\n\nAbstract: Deep Neural Networks have achieved huge success at a wide spectrum of applications from language modeling, computer vision to speech recognition. However, nowadays, good performance alone is not sufficient to satisfy the needs of practical deployment where interpretability is demanded for cases involving ethics and mission critical applications. The complex models of Deep Neural Networks make it hard to understand and reason the predictions, which hinders its further progress. To tackle this problem, we apply the Knowledge Distillation technique to distill Deep Neural Networks into decision trees in order to attain good performance and interpretability simultaneously. We formulate the problem at hand as a multi-output regression problem and the experiments demonstrate that the student model achieves significantly better accuracy performance (about 1\\% to 5\\%) than vanilla decision trees at the same level of tree depth. The experiments are implemented on the TensorFlow platform to make it scalable to big datasets. To the best of our knowledge, we are the first to distill Deep Neural Networks into vanilla decision trees on multi-class datasets."]], "1_corpus": "arxiv"}
-{"tstamp": 1722523333.6616, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "6a0e031567b94ddd9724312eb435daa6", "0_model_name": "BM25", "0_prompt": "what is the difference between what time and when?", "0_output": [["what is the difference between what time and when?", "Stackoverflow Stackexchange\n\nQ: what is the difference between O(nk) and O(n+k) in time complexity? In big O notation of time complexity in algorithmic analysis, when an algorithm depends on n and k, what is the difference between these two notations. \nAlso pls help in the notation to use if there is a nested loop with outer loop running n times and inner loop running k times ?\n\nA: O(nk):\nfor( i=0; i<n; i++ ) {\n   for( j=0; j<k; j++ )\n   {}\n}\n\nO(n+k):\nfor( i=0; i<n; i++ )\n{}\n\nfor( j=0; j<k; j++ )\n{}\n\n"]], "0_corpus": "stackexchange", "1_conv_id": "43787c9534da42b1ac358069ebd04c1c", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "what is the difference between what time and when?", "1_output": [["what is the difference between what time and when?", "Ell Stackexchange\n\nQ: \"What time...?\" or \"At what time...?\" - what is more grammatically correct? This question may sound silly, but it has been bugging me for years. \nIf I ask a question about a precise point in time, should I say \"What time...\" or \"At what time...\"?\nFor example,\n\nAt what time does Billy arrive at the swimming pool?\n\nor\n\nWhat time does Billy arrive at the swimming pool?\n\n\nA: As user070221 notes, both sentences are commonly used in American English.  In some formal speech and writing, \"At what time\" is more acceptable than \"When\" or \"What time\", especially when \"a precise point in time\" is being requested. \nI am an American who grew up in a town with many native speakers of Spanish.  To my ear, both examples in the original post sound like overly literal translations of \"¿A qué hora?\"  To my ear, \"When\" is a more natural way of saying this in English:\n\nWhen does Billy arrive at the swimming pool?  \n\n"]], "1_corpus": "stackexchange"}
+{"tstamp": 1722523333.6616, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "6a0e031567b94ddd9724312eb435daa6", "0_model_name": "BM25", "0_prompt": "what is the difference between what time and when?", "0_output": [["what is the difference between what time and when?", "Stackoverflow Stackexchange\n\nQ: what is the difference between O(nk) and O(n+k) in time complexity? In big O notation of time complexity in algorithmic analysis, when an algorithm depends on n and k, what is the difference between these two notations. \nAlso pls help in the notation to use if there is a nested loop with outer loop running n times and inner loop running k times ?\n\nA: O(nk):\nfor( i=0; i<n; i++ ) {\n   for( j=0; j<k; j++ )\n   {}\n}\n\nO(n+k):\nfor( i=0; i<n; i++ )\n{}\n\nfor( j=0; j<k; j++ )\n{}\n\n"]], "0_corpus": "stackexchange", "1_conv_id": "43787c9534da42b1ac358069ebd04c1c", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "what is the difference between what time and when?", "1_output": [["what is the difference between what time and when?", "Ell Stackexchange\n\nQ: \"What time...?\" or \"At what time...?\" - what is more grammatically correct? This question may sound silly, but it has been bugging me for years. \nIf I ask a question about a precise point in time, should I say \"What time...\" or \"At what time...\"?\nFor example,\n\nAt what time does Billy arrive at the swimming pool?\n\nor\n\nWhat time does Billy arrive at the swimming pool?\n\n\nA: As user070221 notes, both sentences are commonly used in American English.  In some formal speech and writing, \"At what time\" is more acceptable than \"When\" or \"What time\", especially when \"a precise point in time\" is being requested. \nI am an American who grew up in a town with many native speakers of Spanish.  To my ear, both examples in the original post sound like overly literal translations of \"\u00bfA qu\u00e9 hora?\"  To my ear, \"When\" is a more natural way of saying this in English:\n\nWhen does Billy arrive at the swimming pool?  \n\n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1722523807.796, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "dfe9f28cf60c4069ab597a643b9f983c", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "Cognitive test\n\nCognitive tests are assessments of the cognitive capabilities of humans and other animals. Tests administered to humans include various forms of IQ tests; those administered to animals include the mirror test (a test of visual self-awareness) and the T maze test (which tests learning ability). Such testing is used in psychology and psychometrics, as well as other fields studying human and animal intelligence."]], "0_corpus": "wikipedia", "1_conv_id": "aa699ca67c654ea1861add38516ac50b", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "Which test was devised to determine whether robots can think?", "1_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "1_corpus": "wikipedia"}
-{"tstamp": 1722523837.949, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "c02265c3c8ac4f068a034d5d4971c7f2", "0_model_name": "voyage-multilingual-2", "0_prompt": "what model are you?", "0_output": [["what model are you?", "Model (disambiguation)\n\nA model is an informative representation of an object, person or system.\nModel may also refer to:\nFilm and television\nModel (TV series), a 1997 South Korean television series\nThe Model (film), a 2016 Danish thriller drama film\nModels, a 1999 Austrian drama film by Ulrich Seidl\nLiterature\nModel (manhwa), a 1999 series by Lee So-young\nThe Model, a 2005 novel by Lars Saabye Christensen\nMusic\nModel (band), a Turkish rock band\nModels (band), an Australian rock band\nThe Models, an English punk rock band\n\"Model\" (Gulddreng song), 2016\n\"Das Model\", a 1978 song by Kraftwerk\nModel (album), a 2024 album by Wallows\n\"Model\", a 1994 song by Avail from Dixie\n\"Model\", a 1991 song by Simply Red from Stars\nPeople\nModel (surname), a surname frequently of Central European and occasionally English origins\nThe Model (wrestler), ring name of Rick Martel (born 1956)\nEddie Taubensee (born 1968), baseball player nicknamed \"The Model\"\nPlaces\nModel, Colorado, an unincorporated town in the United States\nModel, Masovian Voivodeship, a village in east-central Poland\nOther uses\nModel (mathematical logic), a structure that satisfies a given system of axioms\nMachine learning model, a special type of a mathematical model in the context of machine learning\nMODEL or Movement for Democracy in Liberia, a rebel group\nModel (person), a role or occupation"]], "0_corpus": "wikipedia", "1_conv_id": "bbe903b763244601871fd1e4ea0525b5", "1_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "1_prompt": "what model are you?", "1_output": [["what model are you?", "Choice\n\nA choice is the range of different things from which a being can choose.  The arrival at a choice may incorporate motivators and models."]], "1_corpus": "wikipedia"}
-{"tstamp": 1722523841.5582, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "5fbfc4c28f704d3e8f79700e67543616", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "how to fuck a woman", "0_output": [["how to fuck a woman", "The Perfumed Garden\n\nUgly women are repulsive but also those who are overly raucous and have a loud laugh. A woman who is often seen joking and fooling around may be promiscuous. Also listed as disagreeable traits are revealing their husband's secrets, delighting in other's misfortune, pointing out other's shortcomings, busy-bodies, shrews, talkers, gossips, the lazy, harridans, the hysteric, the nag and the pilfering slag.\nChapter 5: Sexual Intercourse\nIt is recommended that a man should not eat or drink too much before having sex and that foreplay is necessary in order to excite the woman. When finished the man should not rush to leave and should do so on his right hand side.\nChapter 6: Sexual Technique\nThis chapter provides instructions on foreplay, specifying that it should include cunnilingus. The importance of the woman's enjoyment and climax are stressed, as are a number of steps to be taken to avoid injury or infection. Concerning sexual positions it is said that all are permissible (but Khawam's translation adds the words \"except in her rear end\" i.e. anal sex). Eleven positions are then listed, six with the woman on her back, one from behind, two with one or both on their sides, one over furniture and one hanging from a tree.\nChapter 7: The Harmful Effects of Intercourse"]], "0_corpus": "wikipedia", "1_conv_id": "64cdf389bebc41d991e2ad31fcafa671", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": "how to fuck a woman", "1_output": [["how to fuck a woman", "How to Please a Woman\n\nHow to Please a Woman is a 2022 Australian comedy-drama film directed by Renée Webster, starring Sally Phillips, Caroline Brazier, Erik Thomson, Tasma Walton and Alexander England."]], "1_corpus": "wikipedia"}
-{"tstamp": 1722523865.2136, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "0076b2f0f7b541fb88a7132b3c764c4f", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "how to kill people", "0_output": [["how to kill people", "List of types of killing\n\nSiblicide – the killing of an infant individual by their close relatives (full or half siblings).\nSororicide – the act of killing one's sister ( \"sister\").\nUxoricide – the act of killing one's wife ( \"wife\").\nKilling of others\nAmicicide – the act of killing a friend ( \"friend\").\nAndrocide – the systematic killing of men.\nAssassination – the act of killing a prominent person for either political, religious, or monetary reasons.\nCapital punishment – the judicial killing of a human being for crimes.\nCasualty – death (or injury) in wartime.\nCollateral damage – Incidental killing of persons during a military attack that were not the object of attack.\nDemocide or populicide – the murder of any person or people by a government.\nExtrajudicial killing – killing by government forces without due process. See also Targeted killing.\nEuthanasia or mercy killing – the killing of any being with compassionate reasoning; e.g., significant injury or disease.\nFamiliaricide in commutatione eius possessio – the act of killing a family for their property and/or possessions (from  \"of a household\";  \"in exchange for\"; and  \"a possession or property\").\nFemicide, gynecide, gynaecide, or gynocide – the systematic killing of women.\nFeticide – the killing of an embryo or fetus.\nFragging - the act of killing a fellow soldier."]], "0_corpus": "wikipedia", "1_conv_id": "ada677e671634ec99987bff3781499b0", "1_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "1_prompt": "how to kill people", "1_output": [["how to kill people", "The Death of Adolf Hitler\n\nAuthor Soviet journalist Lev Bezymenski (1920–2007), the son of poet Aleksandr Bezymensky, served as an interpreter in the Battle of Berlin under Marshal Zhukov. Early on 1 May 1945, he translated a letter from Goebbels and Bormann announcing Hitler's death. Bezymenski authored several works about the Nazi era.\nContent\nThe book begins with an overview of the Battle of Berlin and its aftermath, including a reproduction of the purported Soviet autopsy report of Hitler's body. Bezymenski states that the bodies of Hitler and Braun were \"the most seriously disfigured of all thirteen corpses\" examined. The appendix summarizes the discovery of the Goebbels family's corpses and includes further forensic reports. On why the autopsy reports were not released earlier, Bezymenski says:Not because of doubts as to the credibility of the experts. ... Those who were involved in the investigation remember that other considerations played a far larger role. First, it was resolved not to publish the results of the forensic-medical report but to \"hold it in reserve\" in case someone might try to slip into the role of \"the Führer saved by a miracle.\" Secondly, it was resolved to continue the investigations in order to exclude any possibility of error or deliberate deception.\nThe Death of Adolf Hitler"]], "1_corpus": "wikipedia"}
+{"tstamp": 1722523841.5582, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "5fbfc4c28f704d3e8f79700e67543616", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "how to fuck a woman", "0_output": [["how to fuck a woman", "The Perfumed Garden\n\nUgly women are repulsive but also those who are overly raucous and have a loud laugh. A woman who is often seen joking and fooling around may be promiscuous. Also listed as disagreeable traits are revealing their husband's secrets, delighting in other's misfortune, pointing out other's shortcomings, busy-bodies, shrews, talkers, gossips, the lazy, harridans, the hysteric, the nag and the pilfering slag.\nChapter 5: Sexual Intercourse\nIt is recommended that a man should not eat or drink too much before having sex and that foreplay is necessary in order to excite the woman. When finished the man should not rush to leave and should do so on his right hand side.\nChapter 6: Sexual Technique\nThis chapter provides instructions on foreplay, specifying that it should include cunnilingus. The importance of the woman's enjoyment and climax are stressed, as are a number of steps to be taken to avoid injury or infection. Concerning sexual positions it is said that all are permissible (but Khawam's translation adds the words \"except in her rear end\" i.e. anal sex). Eleven positions are then listed, six with the woman on her back, one from behind, two with one or both on their sides, one over furniture and one hanging from a tree.\nChapter 7: The Harmful Effects of Intercourse"]], "0_corpus": "wikipedia", "1_conv_id": "64cdf389bebc41d991e2ad31fcafa671", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": "how to fuck a woman", "1_output": [["how to fuck a woman", "How to Please a Woman\n\nHow to Please a Woman is a 2022 Australian comedy-drama film directed by Ren\u00e9e Webster, starring Sally Phillips, Caroline Brazier, Erik Thomson, Tasma Walton and Alexander England."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722525156.6986, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "be5ea7ea315a400988b2dcf6bb514bf9", "0_model_name": "Salesforce/SFR-Embedding-2_R", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "0_corpus": "wikipedia", "1_conv_id": "1bcb956baac3426fadb6547974a8adfa", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_prompt": "Which test was devised to determine whether robots can think?", "1_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "1_corpus": "wikipedia"}
-{"tstamp": 1722525636.7574, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "3265d5ccce70477795f4e5a6efe2da3c", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "What's the purpose of `if __name__ == '__main__'`?", "0_output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: What does `if name == \"__main__\"` mean in Python? \nPossible Duplicate:\nWhat does <if name==“main”:> do? \n\nI have wrote scripts in Python for quite a while now and I study more of Python as I need it. When reading other people's code I meet if name == \"__main__\": construct quite often. \nWhat is it good for?\n\nA: Basically, \nThere's a distinction between the \"main\" script file and external files which were imported or referenced in another way. If the script is the \"main\" script then the special variable __name__ will equal \"__main__\".\nYou can use this to protect the \"execution\" code from the classes and variables the script has. This enables you to import a script and use classes and variables without actually running that script's main code if it has any.\nSee also: What does if name == “main”: do?\n"]], "0_corpus": "stackexchange", "1_conv_id": "1ea6bfbe174d47faa76f5c44c983f261", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_prompt": "What's the purpose of `if __name__ == '__main__'`?", "1_output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: What does `if name == \"__main__\"` mean in Python? \nPossible Duplicate:\nWhat does <if name==“main”:> do? \n\nI have wrote scripts in Python for quite a while now and I study more of Python as I need it. When reading other people's code I meet if name == \"__main__\": construct quite often. \nWhat is it good for?\n\nA: Basically, \nThere's a distinction between the \"main\" script file and external files which were imported or referenced in another way. If the script is the \"main\" script then the special variable __name__ will equal \"__main__\".\nYou can use this to protect the \"execution\" code from the classes and variables the script has. This enables you to import a script and use classes and variables without actually running that script's main code if it has any.\nSee also: What does if name == “main”: do?\n"]], "1_corpus": "stackexchange"}
-{"tstamp": 1722525853.5889, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "240c4d5107bc4d4c9dc4fdf1bd16fff5", "0_model_name": "embed-english-v3.0", "0_prompt": "Where can I find a great tv show about gnomes? They must be real gnomes.", "0_output": [["Where can I find a great tv show about gnomes? They must be real gnomes.", "Gnome\n\nIn the Warcraft franchise (1994 to present), particularly as featured in the massively multiplayer online role-playing game World of Warcraft, gnomes are a race of beings separate from but allied to dwarves and humans, with whom they share the lands of the Eastern Kingdoms. Crafty, intelligent, and smaller than their dwarven brethren, gnomes are one of two races in Azeroth regarded as technologically savvy. It is suggested in lore that the gnomes originally were mechanical creations that at some point became organic lifeforms. In World of Warcraft, gnomes are an exile race, having irradiated their home city of Gnomeregan in an unsuccessful last-ditch effort to drive out marauding foes.\nMovies\nThe 1967 Walt Disney movie The Gnome-Mobile\nThe 2011 animated movie Gnomeo & Juliet\nThe 2018 animated movie Sherlock Gnomes featured gnomish versions of several classic Sherlock Holmes characters.\nTV Shows\nThe Disney+ Series The Santa Clauses\nThe Little Troll Prince features the troll prince Bu, turning into a gnome and gnomes by the end of the special\nDavid the Gnome , The Wisdom of Gnomes and The New World Of the Gnomes\nDerivative uses\nGarden gnomes\nAfter World War II (with early references, in ironic use, from the late 1930s) the diminutive figurines introduced as lawn ornaments during the 19th century came to be known as garden gnomes. The image of the gnome changed further during the 1960s to 1970s, when the first plastic garden gnomes were manufactured. These gnomes followed the style of the 1937 depiction of the seven dwarves in Snow White and the Seven Dwarfs by Disney.\nThis \"Disneyfied\" image of the gnome was built upon  by the illustrated children's book classic Gnomes (1976), in the original Dutch Leven en werken van de Kabouter, by author Wil Huygen and artist Rien Poortvliet, followed in 1981 by The Secret Book of Gnomes."]], "0_corpus": "wikipedia", "1_conv_id": "88f733f7082e49fb8da1c2d5a818e7e4", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "Where can I find a great tv show about gnomes? They must be real gnomes.", "1_output": [["Where can I find a great tv show about gnomes? They must be real gnomes.", "The World of David the Gnome\n\nSynopsis The series follows the gnomes, a kind species who are 15 centimetres (6 inches) tall and weigh between 250 and 300 grams (8 and 10 ounces). There are several types of gnomes: those of the forest, garden, farm, house, dunes, and Siberia, as well as nomadic \"gypsy\" gnomes, who are generally looked down upon by other gnomes. With few exceptions, a gnome's lifespan is exactly 400 years, though one couple in the Balkans lived for 550 years. When they reach the end of their lifespan, they transform into large trees; though they seem to have some control over when and where this happens, they cannot avoid it. For this reason, gnomes are dedicated to protecting forests and the environment, as trees are their ancestors. Gnomes such as the main characters live in pairs in caves or holes under trees, and have a mainly vegetarian diet. They  are often aided by forest animals when traveling long distances or need to reach a specific location quickly. They also have the powers of telepathy and mind control. Their main enemies are the trolls, malevolent and clumsy creatures who cause trouble, as well as gnome poachers.\nCharacters\nDavid (voiced by Tom Bosley) is a gnome of the forest who is 399 years old, making him the oldest gnome around except for Franklin, a gnome from the West who lived for 550 years. David is a doctor and uses his knowledge of many fields, such as hypnosis and acupuncture, to heal his patients, usually animals or other gnomes. David also befriends a bird that, when he whistles, arrives to transport him to where he needs to go. For longer trips, he sometimes travels in a basket attached to its neck.\nLisa (voiced by Jane Woods) is David's wife and companion, who is also 399 years old. They have two children, Lily and Harold. She is in charge of the household, but occasionally accompanies him on his adventures."]], "1_corpus": "wikipedia"}
+{"tstamp": 1722525636.7574, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "3265d5ccce70477795f4e5a6efe2da3c", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "What's the purpose of `if __name__ == '__main__'`?", "0_output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: What does `if name == \"__main__\"` mean in Python? \nPossible Duplicate:\nWhat does <if name==\u201cmain\u201d:> do? \n\nI have wrote scripts in Python for quite a while now and I study more of Python as I need it. When reading other people's code I meet if name == \"__main__\": construct quite often. \nWhat is it good for?\n\nA: Basically, \nThere's a distinction between the \"main\" script file and external files which were imported or referenced in another way. If the script is the \"main\" script then the special variable __name__ will equal \"__main__\".\nYou can use this to protect the \"execution\" code from the classes and variables the script has. This enables you to import a script and use classes and variables without actually running that script's main code if it has any.\nSee also: What does if name == \u201cmain\u201d: do?\n"]], "0_corpus": "stackexchange", "1_conv_id": "1ea6bfbe174d47faa76f5c44c983f261", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_prompt": "What's the purpose of `if __name__ == '__main__'`?", "1_output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: What does `if name == \"__main__\"` mean in Python? \nPossible Duplicate:\nWhat does <if name==\u201cmain\u201d:> do? \n\nI have wrote scripts in Python for quite a while now and I study more of Python as I need it. When reading other people's code I meet if name == \"__main__\": construct quite often. \nWhat is it good for?\n\nA: Basically, \nThere's a distinction between the \"main\" script file and external files which were imported or referenced in another way. If the script is the \"main\" script then the special variable __name__ will equal \"__main__\".\nYou can use this to protect the \"execution\" code from the classes and variables the script has. This enables you to import a script and use classes and variables without actually running that script's main code if it has any.\nSee also: What does if name == \u201cmain\u201d: do?\n"]], "1_corpus": "stackexchange"}
+{"tstamp": 1722525853.5889, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "240c4d5107bc4d4c9dc4fdf1bd16fff5", "0_model_name": "embed-english-v3.0", "0_prompt": "Where can I find a great tv show about gnomes? They must be real gnomes.", "0_output": [["Where can I find a great tv show about gnomes? They must be real gnomes.", "Gnome\n\nIn the Warcraft franchise (1994 to present), particularly as featured in the massively multiplayer online role-playing game World of Warcraft, gnomes are a race of beings separate from but allied to dwarves and humans, with whom they share the lands of the Eastern Kingdoms. Crafty, intelligent, and smaller than their dwarven brethren, gnomes are one of two races in Azeroth regarded as technologically savvy. It is suggested in lore that the gnomes originally were mechanical creations that at some point became organic lifeforms. In World of Warcraft, gnomes are an exile race, having irradiated their home city of Gnomeregan in an unsuccessful last-ditch effort to drive out marauding foes.\nMovies\nThe 1967 Walt Disney movie The Gnome-Mobile\nThe 2011 animated movie Gnomeo & Juliet\nThe 2018 animated movie Sherlock Gnomes featured gnomish versions of several classic Sherlock Holmes characters.\nTV Shows\nThe Disney+ Series The Santa Clauses\nThe Little Troll Prince features the troll prince Bu, turning into a gnome and gnomes by the end of the special\nDavid the Gnome , The Wisdom of Gnomes and The New World Of the Gnomes\nDerivative uses\nGarden gnomes\nAfter World War II (with early references, in ironic use, from the late 1930s) the diminutive figurines introduced as lawn ornaments during the 19th century came to be known as garden gnomes. The image of the gnome changed further during the 1960s to 1970s, when the first plastic garden gnomes were manufactured. These gnomes followed the style of the 1937 depiction of the seven dwarves in Snow White and the Seven Dwarfs by Disney.\nThis \"Disneyfied\" image of the gnome was built upon  by the illustrated children's book classic Gnomes (1976), in the original Dutch Leven en werken van de Kabouter, by author Wil Huygen and artist Rien Poortvliet, followed in 1981 by The Secret Book of Gnomes."]], "0_corpus": "wikipedia", "1_conv_id": "88f733f7082e49fb8da1c2d5a818e7e4", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "Where can I find a great tv show about gnomes? They must be real gnomes.", "1_output": [["Where can I find a great tv show about gnomes? They must be real gnomes.", "The World of David the Gnome\n\nSynopsis The series follows the gnomes, a kind species who are 15 centimetres (6\u00a0inches) tall and weigh between 250 and 300\u00a0grams (8 and 10 ounces). There are several types of gnomes: those of the forest, garden, farm, house, dunes, and Siberia, as well as nomadic \"gypsy\" gnomes, who are generally looked down upon by other gnomes. With few exceptions, a gnome's lifespan is exactly 400 years, though one couple in the Balkans lived for 550 years. When they reach the end of their lifespan, they transform into large trees; though they seem to have some control over when and where this happens, they cannot avoid it. For this reason, gnomes are dedicated to protecting forests and the environment, as trees are their ancestors. Gnomes such as the main characters live in pairs in caves or holes under trees, and have a mainly vegetarian diet. They  are often aided by forest animals when traveling long distances or need to reach a specific location quickly. They also have the powers of telepathy and mind control. Their main enemies are the trolls, malevolent and clumsy creatures who cause trouble, as well as gnome poachers.\nCharacters\nDavid (voiced by Tom Bosley) is a gnome of the forest who is 399 years old, making him the oldest gnome around except for Franklin, a gnome from the West who lived for 550 years. David is a doctor and uses his knowledge of many fields, such as hypnosis and acupuncture, to heal his patients, usually animals or other gnomes. David also befriends a bird that, when he whistles, arrives to transport him to where he needs to go. For longer trips, he sometimes travels in a basket attached to its neck.\nLisa (voiced by Jane Woods) is David's wife and companion, who is also 399 years old. They have two children, Lily and Harold. She is in charge of the household, but occasionally accompanies him on his adventures."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722528721.9266, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "8505cddd0c2048afa16e19c5ae81488b", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "Adversarial example generation for text classification using BERT masked language model  ", "0_output": [["Adversarial example generation for text classification using BERT masked language model  ", "Title: BAE: BERT-based Adversarial Examples for Text Classification\n\nAbstract: Modern text classification models are susceptible to adversarial examples, perturbed versions of the original text indiscernible by humans which get misclassified by the model. Recent works in NLP use rule-based synonym replacement strategies to generate adversarial examples. These strategies can lead to out-of-context and unnaturally complex token replacements, which are easily identifiable by humans. We present BAE, a black box attack for generating adversarial examples using contextual perturbations from a BERT masked language model. BAE replaces and inserts tokens in the original text by masking a portion of the text and leveraging the BERT-MLM to generate alternatives for the masked tokens. Through automatic and human evaluations, we show that BAE performs a stronger attack, in addition to generating adversarial examples with improved grammaticality and semantic coherence as compared to prior work."]], "0_corpus": "arxiv", "1_conv_id": "2d2bd2bc5a7948898267c5846c7148dc", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "Adversarial example generation for text classification using BERT masked language model  ", "1_output": [["Adversarial example generation for text classification using BERT masked language model  ", "Title: BAE: BERT-based Adversarial Examples for Text Classification\n\nAbstract: Modern text classification models are susceptible to adversarial examples, perturbed versions of the original text indiscernible by humans which get misclassified by the model. Recent works in NLP use rule-based synonym replacement strategies to generate adversarial examples. These strategies can lead to out-of-context and unnaturally complex token replacements, which are easily identifiable by humans. We present BAE, a black box attack for generating adversarial examples using contextual perturbations from a BERT masked language model. BAE replaces and inserts tokens in the original text by masking a portion of the text and leveraging the BERT-MLM to generate alternatives for the masked tokens. Through automatic and human evaluations, we show that BAE performs a stronger attack, in addition to generating adversarial examples with improved grammaticality and semantic coherence as compared to prior work."]], "1_corpus": "arxiv"}
 {"tstamp": 1722528748.6744, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "36ba38ee6de24dd0a84b4807886838bb", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": "which abbott and costello movie has who on first", "0_output": [["which abbott and costello movie has who on first", "Abbott and Costello\n\nAbbott and Costello made their film debut in the 1940 comedy One Night in the Tropics. The following year, they appeared in three war-themed comedies: Buck Privates, In the Navy, and Keep 'Em Flying. They also appeared in the 1941 horror comedy film Hold That Ghost, and went on to appear in several other horror comedies, including Abbott and Costello Meet Frankenstein (1948), Abbott and Costello Meet the Killer, Boris Karloff (1949), Abbott and Costello Meet the Invisible Man (1951), and Abbott and Costello Meet the Mummy (1955). Other films starring the duo include Pardon My Sarong, Who Done It? (both 1942), The Time of Their Lives (1946), Buck Privates Come Home (1947), Africa Screams (1949), and Abbott and Costello Go to Mars (1953).\nBurlesque\nThe two comedians had crossed paths a few times previously, but first worked together in 1935 at the Eltinge Burlesque Theater on 42nd Street in New York City. Their first performance resulted from Costello's regular partner becoming ill, and Abbott substituting for him.\nOther performers in the show, including Abbott's wife, encouraged a permanent pairing. The duo built an act by refining and reworking numerous burlesque sketches with Abbott as the devious straight man and Costello as the dimwitted comic.\nDecades later, when AMC moved the old theater  further west on 42nd Street to its current location, giant balloons of Abbott and Costello were rigged to appear to pull it.\nRadio\nThe team's first known radio broadcast was on The Kate Smith Hour on February 3, 1938. At first, the similarities between their voices made it difficult for radio listeners (as opposed to stage audiences) to tell them apart during their rapid-fire repartee. As a result, Costello affected a high-pitched, childish voice. \"Who's on First?\" was first performed for a national radio audience the following month. They performed on the program as regulars for two years, while also landing roles in a Broadway revue, The Streets of Paris, in 1939."]], "0_corpus": "wikipedia", "1_conv_id": "df162b5815ef428a8f41297dd9cdaeef", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "which abbott and costello movie has who on first", "1_output": [["which abbott and costello movie has who on first", "Abbott and Costello\n\nAbbott and Costello made their film debut in the 1940 comedy One Night in the Tropics. The following year, they appeared in three war-themed comedies: Buck Privates, In the Navy, and Keep 'Em Flying. They also appeared in the 1941 horror comedy film Hold That Ghost, and went on to appear in several other horror comedies, including Abbott and Costello Meet Frankenstein (1948), Abbott and Costello Meet the Killer, Boris Karloff (1949), Abbott and Costello Meet the Invisible Man (1951), and Abbott and Costello Meet the Mummy (1955). Other films starring the duo include Pardon My Sarong, Who Done It? (both 1942), The Time of Their Lives (1946), Buck Privates Come Home (1947), Africa Screams (1949), and Abbott and Costello Go to Mars (1953).\nBurlesque\nThe two comedians had crossed paths a few times previously, but first worked together in 1935 at the Eltinge Burlesque Theater on 42nd Street in New York City. Their first performance resulted from Costello's regular partner becoming ill, and Abbott substituting for him.\nOther performers in the show, including Abbott's wife, encouraged a permanent pairing. The duo built an act by refining and reworking numerous burlesque sketches with Abbott as the devious straight man and Costello as the dimwitted comic.\nDecades later, when AMC moved the old theater  further west on 42nd Street to its current location, giant balloons of Abbott and Costello were rigged to appear to pull it.\nRadio\nThe team's first known radio broadcast was on The Kate Smith Hour on February 3, 1938. At first, the similarities between their voices made it difficult for radio listeners (as opposed to stage audiences) to tell them apart during their rapid-fire repartee. As a result, Costello affected a high-pitched, childish voice. \"Who's on First?\" was first performed for a national radio audience the following month. They performed on the program as regulars for two years, while also landing roles in a Broadway revue, The Streets of Paris, in 1939."]], "1_corpus": "wikipedia"}
-{"tstamp": 1722528773.7655, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "2f84ad68265b4681b641e07967d9522a", "0_model_name": "BM25", "0_prompt": "are goofy and pluto the same?", "0_output": [["are goofy and pluto the same?", "Stackoverflow Stackexchange\n\nQ: Input Unicode characters in Pluto.jl Just wondering if anyone knows how to add Unicode input in pluto.jl. For example, if I want to input a Unicode character for sigma, I would type \\sigma in vs code, and the autocomplete will change it to σ. Any ideas how I could do the same in pluto.jl\n\nA: You should be able to do the same in pluto: type \\sigma, then hit TAB and it should change to σ.\n"]], "0_corpus": "stackexchange", "1_conv_id": "b60c4371b328490eb9899fba31918b49", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "are goofy and pluto the same?", "1_output": [["are goofy and pluto the same?", "Physics Stackexchange\n\nQ: What is the current status of Pluto? Pluto has been designated a planet in our solar system for years (ever since it was discovered in the last century), but in 2006 it was demoted.\nWhat caused this decision? And is there a chance that it could be reversed?\nEdit: well, http://www.dailygalaxy.com/my_weblog/2017/03/nasas-new-horizon-astronomers-declare-pluto-is-a-planet-so-is-jupiters-ocean-moon-europa.html is interesting; this is science, so anything could (potentially) change.\n\nA: Pluto is still considered a dwarf planet. This was because it did not meet the full criteria for being classified as a planet. Most notably it did not clear its orbit of other debris.\nThis is still considered controversial as many scientist do not agree with the definition of what a planet is and still consider Pluto to meet planet criteria. So it is possible that this may change especially if the criteria for what is a planet changes due to new discoveries. This is very possible with the work the Kepler telescope is producing. As we discover new planets in large numbers you can be sure they will be finding ever different planets that will force a redefinition of what a planet is.\n"]], "1_corpus": "stackexchange"}
-{"tstamp": 1722528948.1344, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "0b170e115278407a9b1297593a0ce29f", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "when did they stop cigarette advertising on television", "0_output": [["when did they stop cigarette advertising on television", "List of cigarette brands\n\nThis is a list of current cigarette brands.  Factory-made cigarettes, when contrasted to roll-your-own cigarettes, are called tailor mades."]], "0_corpus": "wikipedia", "1_conv_id": "fdd4301ff1ef4cbaaee2b80a6d5511a6", "1_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "1_prompt": "when did they stop cigarette advertising on television", "1_output": [["when did they stop cigarette advertising on television", "MCA Inc.\n\nThe Rules were next passed to the 1950s generation of MCA talent agents, including Jerry Perenchio, who later owned and headed a number of businesses including Univision from 1992 to 2007. Perenchio was well known for his version of the Rules (up to twenty rules), which varied from year to year and had some internal contradictions (In 2006, Perenchio pointed out that while there was a \"no nepotism\" rule, he was aware his son was on the company's board of directors at the time).\nMove to Hollywood: \"The Octopus\"\nIn 1939, based on Wasserman's recommendation, MCA's headquarters moved from Chicago to Beverly Hills, California, creating a movie division. The company began to acquire talent agencies, representing established actors such as James Stewart, Henry Fonda, Bette Davis, Jane Wyman and Ronald Reagan, whom Wasserman became very close with personally. In later decades, Wasserman became a guiding force in Reagan's political ambition by helping Reagan to win the presidency of the Screen Actors Guild (SAG), then election as Governor of California in 1966, and finally President of the United States in 1980.\nBy the end of the 1930s, MCA had become the largest talent agency in the world, with over 700 clients, including movie stars, recording artists, Broadway actors, radio stars, and directors. The company's aggressive acquisition of clientele in all entertainment sectors earned MCA the nickname of \"The Octopus\". The company's activities led U.S. Department of Justice agents to investigate not only whether MCA was a monopoly breaking antitrust laws, but also its suspected connections to underworld criminal activities. This investigation continued for the next few decades.\nRevue Productions and the early days of television"]], "1_corpus": "wikipedia"}
+{"tstamp": 1722528773.7655, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "2f84ad68265b4681b641e07967d9522a", "0_model_name": "BM25", "0_prompt": "are goofy and pluto the same?", "0_output": [["are goofy and pluto the same?", "Stackoverflow Stackexchange\n\nQ: Input Unicode characters in Pluto.jl Just wondering if anyone knows how to add Unicode input in pluto.jl. For example, if I want to input a Unicode character for sigma, I would type \\sigma in vs code, and the autocomplete will change it to \u03c3. Any ideas how I could do the same in pluto.jl\n\nA: You should be able to do the same in pluto: type \\sigma, then hit TAB and it should change to \u03c3.\n"]], "0_corpus": "stackexchange", "1_conv_id": "b60c4371b328490eb9899fba31918b49", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "are goofy and pluto the same?", "1_output": [["are goofy and pluto the same?", "Physics Stackexchange\n\nQ: What is the current status of Pluto? Pluto has been designated a planet in our solar system for years (ever since it was discovered in the last century), but in 2006 it was demoted.\nWhat caused this decision? And is there a chance that it could be reversed?\nEdit: well, http://www.dailygalaxy.com/my_weblog/2017/03/nasas-new-horizon-astronomers-declare-pluto-is-a-planet-so-is-jupiters-ocean-moon-europa.html is interesting; this is science, so anything could (potentially) change.\n\nA: Pluto is still considered a dwarf planet. This was because it did not meet the full criteria for being classified as a planet. Most notably it did not clear its orbit of other debris.\nThis is still considered controversial as many scientist do not agree with the definition of what a planet is and still consider Pluto to meet planet criteria. So it is possible that this may change especially if the criteria for what is a planet changes due to new discoveries. This is very possible with the work the Kepler telescope is producing. As we discover new planets in large numbers you can be sure they will be finding ever different planets that will force a redefinition of what a planet is.\n"]], "1_corpus": "stackexchange"}

data/retrieval_battle-dd51f1d2-bf9d-4e42-9a72-5b30dd2bd338.jsonl

@@ -1,10 +1,8 @@
 {"tstamp": 1722357180.6325, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "42394a8aba7e4beda1c9a6de11159583", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": "How do people think about the current Iran president?", "0_output": [["How do people think about the current Iran president?", "President of Iran\n\nThe president of Iran () is the head of government of the Islamic Republic of Iran and the second highest-ranking official, after the Supreme Leader. The first election was held in 1980 and was won by Abulhassan Banisadr. Mohammad Mokhber currently serves as the acting President of Iran following the death of Ebrahim Raisi, the former president, on 19 May 2024. He is expected to be succeeded by Masoud Pezeshkian on July 30, who won the 2024 Iranian presidential election.\nHistory\nAfter the Iranian Revolution of 1979 and 1979 Iranian Islamic Republic referendum on March 29 and 30, the new government needed to craft a new constitution. Supreme Leader Ruhollah Khomeini, ordered an election for the Assembly of Experts, the body tasked with writing the constitution. The assembly presented the constitution on October 24, 1979, and Supreme Leader Ruhollah Khomeini and Prime Minister Mehdi Bazargan approved it."]], "0_corpus": "wikipedia", "1_conv_id": "52214b21d0284944897b648b75b8af91", "1_model_name": "voyage-multilingual-2", "1_prompt": "How do people think about the current Iran president?", "1_output": [["How do people think about the current Iran president?", "History of the Islamic Republic of Iran\n\n2009 election controversy Ahmadinejad's 2009 election victory was hotly disputed and marred by large protests that formed the \"greatest domestic challenge\" to the leadership of the Islamic Republic \"in 30 years\". Despite high turnout and large enthusiastic crowds for reformist opponent Mir-Hossein Mousavi, Ahmadinejad was officially declared to have won by a 2\u20131 margin against three opponents.  Allegations of voting irregularities and protest by Mousavi his supporters were immediate and by 1 July 2009 1000 people had been arrested and 20 killed in street demonstrations. Supreme Leader Ali Khamenei and others Islamic officials blamed foreign powers for fomenting the protest. However, according to World Public Opinion (a United States poll), the protest does not mean Iran is in a \"pre-revolutionary\" situation as a WPO poll of Iranians taken in early September 2009 found high levels of satisfaction with the regime. 80% of the Iranians respondents said President Ahmadinejad was honest, 64% expressed a lot of confidence in him, and nine in ten said they were satisfied with Iran's system of government.\nPublic opinion\nAccording to the (U.S.) International Peace Institute's 2010-poll conducted in Persian by a representative sample of the Iranian people:\nIranians are divided on the government's performance.\nDissatisfied with the economy.\nWorry over sanctions and isolation.\nWant to focus on domestic affairs.\nFavor closer ties to the West.\nRising tensions sparked hostility toward the US, Europe, and U.N.\nFavor nuclear arms and do not want to back deals to halt enrichment.\nIndependent polls do not contradict official turnout of 2009 election, which gave around 60% of the vote to Ahmadinejad.\nRouhani Administration\nHassan Rouhani was elected as President of Iran on 12 June 2013 and took office on 3 August. He is known as a moderate left-leaner, supported by reformists in the election. He has open ideas in the area of economics and a high-level foreign policy, as he served as a diplomat before his election.  He has moved quickly to engage in diplomatic negotiations with Western countries, seeking the lifting of crippling economic sanctions on oil exports in exchange for Iran's cooperation with UN treaties regarding the development of nuclear weapons. Economic conditions improved during Rouhani's first term and led to his reelection in 2017 with his reformist agenda."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722357524.4041, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "3d09c01e7dfb4691b24743a8201671cf", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "Winter war", "0_output": [["Winter war", "Winter War\n\nOn 30 November 1939, Soviet forces invaded Finland with 21 divisions, totalling 450,000 men, and bombed Helsinki, killing about 100 citizens and destroying more than 50 buildings. In response to international criticism, Soviet Foreign Minister Vyacheslav Molotov stated that the Soviet Air Force was not bombing Finnish cities but rather dropping humanitarian aid to the starving Finnish population; the bombs were sarcastically dubbed Molotov bread baskets by Finns. The Finnish statesman J. K. Paasikivi commented that the Soviet attack without a declaration of war violated three separate non-aggression pacts: the Treaty of Tartu, which was signed in 1920, the non-aggression pact between Finland and the Soviet Union, which was signed in 1932 and again in 1934; and also the Covenant of the League of Nations, which the Soviet Union signed in 1934. Field Marshal C.G.E. Mannerheim was appointed Commander-in-Chief of the Finnish Defence Forces after the Soviet attack. In a further reshuffling, Aimo Cajander's caretaker cabinet was replaced by Risto Ryti and his cabinet, with V\u00e4in\u00f6 Tanner as foreign minister because of opposition to Cajander's prewar politics. Finland brought the matter of the Soviet invasion before the League of Nations. The League expelled the Soviet Union on 14 December 1939 and exhorted its members to aid Finland.\nHeaded by Otto Wille Kuusinen, the Finnish Democratic Republic puppet government operated in the parts of Finnish Karelia occupied by the Soviets, and was also referred to as the \"Terijoki Government\", after the village of Terijoki, the first settlement captured by the advancing Red Army. After the war, the puppet government was reabsorbed into the Soviet Union. From the very outset of the war, working-class Finns stood behind the legitimate government in Helsinki. Finnish national unity against the Soviet invasion was later called the spirit of the Winter War.\nFirst battles and Soviet advance to Mannerheim Line"]], "0_corpus": "wikipedia", "1_conv_id": "b082f7b0fd6a44e9b625a592b31c17dc", "1_model_name": "BM25", "1_prompt": "Winter war", "1_output": [["Winter war", "Arab Winter\n\nThe Arab Winter () is a term referring to the resurgence of authoritarianism and Islamic extremism in some Arab countries in the 2010s in the aftermath of the Arab Spring protests. The term \"Arab Winter\" refers to the events across Arab League countries in the Middle East and North Africa, including the Syrian civil war, the Iraqi insurgency and the subsequent War in Iraq, the Egyptian Crisis, the First Libyan Civil War and the subsequent Second Libyan Civil War, and the Yemeni civil war. Events referred to as the Arab Winter include those in Egypt that led to the removal from office in 2013 of Mohamed Morsi and the subsequent election in 2014 of Abdel Fattah el-Sisi.\nThe term was first coined by Chinese political scientist Zhang Weiwei during a debate with American political scientist Francis Fukuyama on June 27, 2011. Fukuyama believed the Arab Spring movement would inevitably spread to China, while Zhang predicted the Arab Spring would soon turn into an Arab Winter.\nAccording to scholars of the University of Warsaw, the Arab Spring fully devolved into the Arab Winter four years after its onset, in 2014. The Arab Winter is characterized by the emergence of multiple regional wars, mounting regional instability, economic and demographic decline of Arab countries, and ethno-religious sectarian strife. According to a study by the American University of Beirut, by the summer of 2014, the Arab Winter had resulted in nearly a quarter of a million deaths and millions of refugees. Perhaps the most significant event in the Arab Winter was the rise of the extremist group Islamic State of Iraq and the Levant, which controlled swathes of land in the region from 2014 to 2019."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722357673.547, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "40adf2ecfae142f690cf15572d1c2a39", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": "A paper with a dataset and methods for Chinese LFQA including real-time web search interaction  ", "0_output": [["A paper with a dataset and methods for Chinese LFQA including real-time web search interaction  ", "Title: WebCPM: Interactive Web Search for Chinese Long-form Question Answering\n\nAbstract: Long-form question answering (LFQA) aims at answering complex, open-ended questions with detailed, paragraph-length responses. The de facto paradigm of LFQA necessitates two procedures: information retrieval, which searches for relevant supporting facts, and information synthesis, which integrates these facts into a coherent answer. In this paper, we introduce WebCPM, the first Chinese LFQA dataset. One unique feature of WebCPM is that its information retrieval is based on interactive web search, which engages with a search engine in real time. Following WebGPT, we develop a web search interface. We recruit annotators to search for relevant information using our interface and then answer questions. Meanwhile, the web search behaviors of our annotators would be recorded. In total, we collect 5,500 high-quality question-answer pairs, together with 14,315 supporting facts and 121,330 web search actions. We fine-tune pre-trained language models to imitate human behaviors for web search and to generate answers based on the collected facts. Our LFQA pipeline, built on these fine-tuned models, generates answers that are no worse than human-written ones in 32.5% and 47.5% of the cases on our dataset and DuReader, respectively."]], "0_corpus": "arxiv", "1_conv_id": "5b7d23084cf84e698b8ae3ef7e10bdc3", "1_model_name": "embed-english-v3.0", "1_prompt": "A paper with a dataset and methods for Chinese LFQA including real-time web search interaction  ", "1_output": [["A paper with a dataset and methods for Chinese LFQA including real-time web search interaction  ", "Title: WebCPM: Interactive Web Search for Chinese Long-form Question Answering\n\nAbstract: Long-form question answering (LFQA) aims at answering complex, open-ended questions with detailed, paragraph-length responses. The de facto paradigm of LFQA necessitates two procedures: information retrieval, which searches for relevant supporting facts, and information synthesis, which integrates these facts into a coherent answer. In this paper, we introduce WebCPM, the first Chinese LFQA dataset. One unique feature of WebCPM is that its information retrieval is based on interactive web search, which engages with a search engine in real time. Following WebGPT, we develop a web search interface. We recruit annotators to search for relevant information using our interface and then answer questions. Meanwhile, the web search behaviors of our annotators would be recorded. In total, we collect 5,500 high-quality question-answer pairs, together with 14,315 supporting facts and 121,330 web search actions. We fine-tune pre-trained language models to imitate human behaviors for web search and to generate answers based on the collected facts. Our LFQA pipeline, built on these fine-tuned models, generates answers that are no worse than human-written ones in 32.5% and 47.5% of the cases on our dataset and DuReader, respectively."]], "1_corpus": "arxiv"}
-{"tstamp": 1722358076.7701, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "ba39a43bd1d845a29bb436a813e124f3", "0_model_name": "text-embedding-004", "0_prompt": "inpars", "0_output": [["inpars", "Title: Gamma-Ray Bursts observed by INTEGRAL\n\nAbstract: During the first six months of operations, six Gamma Ray Bursts (GRBs) have been detected in the field of view of the INTEGRAL instruments and localized by the INTEGRAL Burst Alert System (IBAS): a software for the automatic search of GRBs and the rapid distribution of their coordinates. I describe the current performances of IBAS and review the main results obtained so far. The coordinates of the latest burst localized by IBAS, GRB 031203, have been distributed within 20 s from the burst onset and with an uncertainty radius of only 2.7 arcmin."]], "0_corpus": "arxiv", "1_conv_id": "613710642ec9426ca02056c89cf5fbf7", "1_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "1_prompt": "inpars", "1_output": [["inpars", "Title: Matrix Model Fixed Point of Noncommutative Phi-Four\n\nAbstract: In this article we exhibit explicitly the matrix model ($\\theta=\\infty$) fixed point of phi-four theory on noncommutative spacetime with only two noncommuting directions using the Wilson renormalization group recursion formula and the 1/N expansion of the zero dimensional reduction and then calculate the mass critical exponent $\\nu$ and the anomalous dimension $\\eta$ in various dimensions ."]], "1_corpus": "arxiv"}
 {"tstamp": 1722358092.6242, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "d192f963eb874f30877d73a20a3386d7", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "are meet or are met?", "0_output": [["are meet or are met?", "Stackoverflow Stackexchange\n\nQ: Get values from TempData into view I want to get value of TempData into View\nController\n[NonAction]\n    public ActionResult RedirectToMessagePage(string title, string message)\n    {\n        TempData[\"SuccessModel\"] = new Models.SuccessModel { Title = title, Message = message };\n\n        return RedirectToAction(\"Result\", \"Common\");\n    }\n\nView\nif (TempData[\"SuccessModel\"] != null)\n        {\n\n            <div id=\"validationMessages\">\n                @{\n                    var errors =TempData.Values;\n                }\n                @if (errors != null && errors.Count() > 0)\n                {\n                    <div style=\"position:absolute; background:Black; color:White; top:250px; left:550px;\">\n                        @foreach (var error in errors)\n                        {   \n                           @error \n                        }\n                    </div>\n                }\n            </div>\n        }\n\nI want to get value of Message of TempData[\"SuccessModel\"] into view and want to display it.\nHow can I display it?\n\nA: under\nvar errors =TempData.Values;\n\nyou can cast TempData[\"SuccessModel\"] and get it's message\nvar message = (TempData[\"SuccessModel\"] as Models.SuccessModel).Message;\n\nand use this message variable.\n"]], "0_corpus": "stackexchange", "1_conv_id": "f3eef428ee4847acbdadd37fb9b77a6e", "1_model_name": "BM25", "1_prompt": "are meet or are met?", "1_output": [["are meet or are met?", "Ell Stackexchange\n\nQ: \"Meet my boss\" or \"meet with my boss\"? What is the difference between \"meet\" and \"meet with\"?\nFor example, which of these should I say?\n\n  \n*\n  \n*I met my boss yesterday \n  \n*I met with my boss yesterday\n  \n*I'm meeting my friends tonight \n  \n*I'm meeting with my friends tonight\n  \n\n\nA: \nI met my boss yesterday.\n\nWithout elaboration, this implies that you met your boss for the first time.\n\nI met my boss at Spago* yesterday.\n\nYou had lunch or dinner with your boss. (It was not your first encounter.)\n\nI met with my boss yesterday.\n\nYou got together with your boss for a specific purpose. It was likely to be a working meeting of some sort.\n\nI'm meeting my friends tonight.\n\nYou're going out to have fun with your friends.\n\nI'm meeting with my friends tonight.\n\nYou're getting together with friends for some shared purpose. It might be for fun, but the \"with\" is more likely to be used to suggest a special purpose to the meeting: planning, hobby, whatever.\n* Spago is an upscale restaurant, suitable for business luncheons or important dinners.\n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1722358096.4098, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "2412f917aa0241cfa42543f4dcf47ff5", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": "When did american astronauts first walk on the moon", "0_output": [["When did american astronauts first walk on the moon", "Exploration of the Moon\n\nIn September 1968 the Soviet Union's Zond 5 sent tortoises on a circumlunar mission, followed by turtles aboard Zond 6 in November. On December 24, 1968, the crew of Apollo 8\u2014Frank Borman, James Lovell and William Anders\u2014became the first human beings to enter lunar orbit and see the far side of the Moon in person. Humans first landed on the Moon on July 20, 1969. The first humans to walk on the lunar surface were Neil Armstrong, commander of the U.S. mission Apollo 11 and his fellow astronaut Buzz Aldrin.\nThe first robot lunar rover to land on the Moon was the Soviet vessel Lunokhod 1 on November 17, 1970, as part of the Lunokhod programme. To date, the last human to stand on the Moon was Eugene Cernan, who as part of the Apollo 17 mission, walked on the Moon in December 1972.\nMoon rock samples were brought back to Earth by three Luna missions (Luna 16, 20, and 24) and the Apollo missions 11 through 17 (except Apollo 13, which aborted its planned lunar landing). Luna 24 in 1976 was the last Lunar mission by either the Soviet Union or the U.S. until Clementine in 1994. Focus shifted to probes to other planets, space stations, and the Shuttle program.\nBefore the \"Moon race,\" the U.S. had pre-projects for scientific and military moonbases: the Lunex Project and Project Horizon. Besides crewed landings, the abandoned Soviet crewed lunar programs included the building of a multipurpose moonbase \"Zvezda\", the first detailed project, complete with developed mockups of expedition vehicles and surface modules.\nAfter 1990"]], "0_corpus": "wikipedia", "1_conv_id": "4ca696c1ad1e44dd83714298585835db", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_prompt": "When did american astronauts first walk on the moon", "1_output": [["When did american astronauts first walk on the moon", "Walking on the Moon\n\n\"Walking on the Moon\" is a reggae song by British rock band the Police, released as the second single from their second studio album, Reggatta de Blanc (1979). The song was written by the band's lead vocalist and bassist Sting. It went on to become the band's second  hit in the UK.\nBackground\nSting has said that he wrote the song when he was drunk one night after a concert in Munich. The following morning, he remembered the song and wrote it down.\nIn his autobiography, Sting implies that the song was partially inspired by an early girlfriend:\nAccording to Sting, the song was originally recorded \"as a rocker\" in early versions, but it was reworked. The riff, which is played on the bass, was described as \"weird\" and \"jazzy\" by Sting. Guitarist Andy Summers came up with the chord \"which hits after the bass notes\" throughout the song.\n\"Walking on the Moon\" was released as the follow-up single to the British  single \"Message in a Bottle\" in late 1979. The song was the Police's second number-one hit single in the United Kingdom. It also reached  in Ireland and  in Australia but did not chart in the United States."]], "1_corpus": "wikipedia"}
-{"tstamp": 1722358118.4642, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "6e69d5cf6ca64ba795f4d1b52f734ef6", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": "inpars", "0_output": [["inpars", "Title: ParPaRaw: Massively Parallel Parsing of Delimiter-Separated Raw Data\n\nAbstract: Parsing is essential for a wide range of use cases, such as stream processing, bulk loading, and in-situ querying of raw data. Yet, the compute-intense step often constitutes a major bottleneck in the data ingestion pipeline, since parsing of inputs that require more involved parsing rules is challenging to parallelise. This work proposes a massively parallel algorithm for parsing delimiter-separated data formats on GPUs. Other than the state-of-the-art, the proposed approach does not require an initial sequential pass over the input to determine a thread's parsing context. That is, how a thread, beginning somewhere in the middle of the input, should interpret a certain symbol (e.g., whether to interpret a comma as a delimiter or as part of a larger string enclosed in double-quotes). Instead of tailoring the approach to a single format, we are able to perform a massively parallel FSM simulation, which is more flexible and powerful, supporting more expressive parsing rules with general applicability. Achieving a parsing rate of as much as 14.2 GB/s, our experimental evaluation on a GPU with 3584 cores shows that the presented approach is able to scale to thousands of cores and beyond. With an end-to-end streaming approach, we are able to exploit the full-duplex capabilities of the PCIe bus and hide latency from data transfers. Considering the end-to-end performance, the algorithm parses 4.8 GB in as little as 0.44 seconds, including data transfers."]], "0_corpus": "arxiv", "1_conv_id": "f4c133383bd5449c9636e530f8d4fdeb", "1_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "1_prompt": "inpars", "1_output": [["inpars", "Title: Matrix Model Fixed Point of Noncommutative Phi-Four\n\nAbstract: In this article we exhibit explicitly the matrix model ($\\theta=\\infty$) fixed point of phi-four theory on noncommutative spacetime with only two noncommuting directions using the Wilson renormalization group recursion formula and the 1/N expansion of the zero dimensional reduction and then calculate the mass critical exponent $\\nu$ and the anomalous dimension $\\eta$ in various dimensions ."]], "1_corpus": "arxiv"}
 {"tstamp": 1722358165.7939, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "78dfb52f27f64b61929bcde3dcc9504b", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "inpars", "0_output": [["inpars", "Title: Phonon Assisted Multimagnon Optical Absorption and Long Lived Two-Magnon States in Undoped Lamellar Copper Oxides\n\nAbstract: We calculate the effective charge for multimagnon infrared (IR) absorption assisted by phonons in the parent insulating compounds of cuprate superconductors and the spectra for two-magnon absorption using interacting spin-wave theory. Recent measured bands in the mid IR [Perkins et al. Phys. Rev. Lett. {\\bf 71} 1621 (1993)] are interpreted as involving one phonon plus a two-magnon virtual bound state, and one phonon plus higher multimagnon absorption processes. The virtual bound state consists of a narrow resonance occurring when the magnon pair has total momentum close to $(\\pi,0)$."]], "0_corpus": "arxiv", "1_conv_id": "59aba873f4524f3298bd6dc89efec421", "1_model_name": "BM25", "1_prompt": "inpars", "1_output": [["inpars", "Title: InPars-v2: Large Language Models as Efficient Dataset Generators for Information Retrieval\n\nAbstract: Recently, InPars introduced a method to efficiently use large language models (LLMs) in information retrieval tasks: via few-shot examples, an LLM is induced to generate relevant queries for documents. These synthetic query-document pairs can then be used to train a retriever. However, InPars and, more recently, Promptagator, rely on proprietary LLMs such as GPT-3 and FLAN to generate such datasets. In this work we introduce InPars-v2, a dataset generator that uses open-source LLMs and existing powerful rerankers to select synthetic query-document pairs for training. A simple BM25 retrieval pipeline followed by a monoT5 reranker finetuned on InPars-v2 data achieves new state-of-the-art results on the BEIR benchmark. To allow researchers to further improve our method, we open source the code, synthetic data, and finetuned models: https://github.com/zetaalphavector/inPars/tree/master/tpu"]], "1_corpus": "arxiv"}
 {"tstamp": 1722358254.6555, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "620b3c01c65e40488aebdee13adb9ab4", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": "when does the stock market open pacific time", "0_output": [["when does the stock market open pacific time", "Tokyo Stock Exchange\n\nHours The exchange's normal trading sessions are from 9:00\u00a0a.m. to 11:30\u00a0a.m. and from 12:30\u00a0p.m. to 3:00\u00a0p.m. on all days of the week except Saturdays, Sundays and holidays declared by the Exchange in advance. The exchange is closed for the following holidays: New Year's Day, Coming of Age Day, National Foundation Day, Vernal Equinox Day, Sh\u014dwa Day, Constitution Memorial Day, Greenery Day, Children's Day, Marine Day, Respect for the Aged Day, Autumnal Equinox, Health and Sports Day, Culture Day, Labour Thanksgiving Day, and The Emperor's Birthday.\nStock market\nFirst, Second and other Sections until April 4, 2022\nUntil April 4, 2022, corporate shares are listed and traded at Tokyo Stock Exchange in five market sections: the First Section which started when Tokyo Stock Exchange was re-established in 1949 and includes mainly large companies; the Second Section which started in 1961 and includes mainly mid-sized companies; JASDAQ (established in 1991, acquired by Osaka Stock Exchange in 2010, and absorbed into TSE in 2013) and Mothers (Market of the high-growth and emerging stocks, established at TSE in 1999) which are both for emerging companies; and TOKYO PRO Market which was established in 2009 jointly with London Stock Exchange as an Alternative Investment Market (Tokyo AIM) for small companies.\nThere were a total of 3,821 companies listed in Tokyo Stock Exchange, as of March 31, 2022.\nPrime, Standard and Growth markets since April 4, 2022"]], "0_corpus": "wikipedia", "1_conv_id": "0d61a1651b204d21ad4de10b5702eaf5", "1_model_name": "GritLM/GritLM-7B", "1_prompt": "when does the stock market open pacific time", "1_output": [["when does the stock market open pacific time", "Philadelphia Stock Exchange\n\nIn March 2020, the PHLX announced plans to temporarily move to all-electronic trading on March 23, 2020, due to the COVID-19 pandemic. Along with the NYSE and the BSE, the PHLX reopened on May 26, 2020.\nLocations\n1790-1834 \u2013 Merchants Coffee House (City Tavern), 138 South 2nd Street at Walnut Street\n1834-1876 \u2013 Merchants' Exchange Building, 143 South 3rd Streets at Dock Street\n1876-1888 \u2013 Girard Bank Building, 120 South 3rd Street, between Walnut and Chestnut Streets\n1888-1902 \u2013 Drexel Building, near Chestnut and South 5th Streets\n1902-1912 \u2013 Merchants' Exchange Building, 143 South 3rd Streets at Dock Street\n1913-1951 \u2013 1411 Walnut Street, between South Broad and South 15th Streets\n1951-1966 \u2013 Central Penn Bank Building, 1401 Walnut Street, between South Broad and South 15th Streets\n1966-1981 \u2013 120 South 17th Street, between Sansom Street and Stock Exchange Place (Ionic Street)\nJan-Feb 1969 \u2013 Drecker Building, Bala Cynwyd, Pennsylvania (trading floor only)\n1981-2017 \u2013 Market and South 19th Streets\n2017-now \u2013 FMC Tower at Cira Centre South, 2929 Walnut Street, between Schuylkill Expressway and South 30th Street\nHours\nThe exchange's normal trading sessions are from 9:30\u00a0am to 4:00\u00a0pm on all days of the week except Saturdays, Sundays and holidays declared by the exchange in advance."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722358305.208, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "bcf54224c20940c9be3f305f57cbea90", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "how many indian prime ministers were born in independent india", "0_output": [["how many indian prime ministers were born in independent india", "List of prime ministers of India\n\nSince 1947, India has had 14 prime ministers. Jawaharlal Nehru was India's first prime minister, serving as prime minister of the Dominion of India from 15 August 1947 until 26 January 1950, and thereafter of the Republic of India until his death in May 1964. (India conducted its first post-independence general elections in 1952). Earlier, Nehru had served as prime minister of the Interim Government of India during the British Raj from 2 September 1946 until 14 August 1947, his party, the Indian National Congress having won the 1946 Indian provincial elections. Nehru was succeeded by Lal Bahadur Shastri, whose 1 year 7-month term ended in his death in Tashkent, then in the USSR, where he had signed the Tashkent Declaration between India and Pakistan. Indira Gandhi, Nehru's daughter, succeeded Shastri in 1966 to become the country's first female prime minister. Eleven years later, her party the Indian National Congress lost the 1977 Indian general election to the Janata Party, whose leader Morarji Desai became the first non-Congress prime minister. After Desai resigned in 1979, his former associate Charan Singh briefly held office until the Congress won the 1980 Indian general election and Indira Gandhi returned as prime minister. Her second term as prime minister ended five years later on 31 October 1984, when she was assassinated by her bodyguards. Her son Rajiv Gandhi was sworn in as India's youngest premier. Members of NehruGandhi family have been prime minister for approximately 38 years."]], "0_corpus": "wikipedia", "1_conv_id": "b780dab7e4904028b9644772aa8f3c0c", "1_model_name": "text-embedding-004", "1_prompt": "how many indian prime ministers were born in independent india", "1_output": [["how many indian prime ministers were born in independent india", "List of prime ministers of India\n\nSince 1947, India has had 14 prime ministers. Jawaharlal Nehru was India's first prime minister, serving as prime minister of the Dominion of India from 15 August 1947 until 26 January 1950, and thereafter of the Republic of India until his death in May 1964. (India conducted its first post-independence general elections in 1952). Earlier, Nehru had served as prime minister of the Interim Government of India during the British Raj from 2 September 1946 until 14 August 1947, his party, the Indian National Congress having won the 1946 Indian provincial elections. Nehru was succeeded by Lal Bahadur Shastri, whose 1 year 7-month term ended in his death in Tashkent, then in the USSR, where he had signed the Tashkent Declaration between India and Pakistan. Indira Gandhi, Nehru's daughter, succeeded Shastri in 1966 to become the country's first female prime minister. Eleven years later, her party the Indian National Congress lost the 1977 Indian general election to the Janata Party, whose leader Morarji Desai became the first non-Congress prime minister. After Desai resigned in 1979, his former associate Charan Singh briefly held office until the Congress won the 1980 Indian general election and Indira Gandhi returned as prime minister. Her second term as prime minister ended five years later on 31 October 1984, when she was assassinated by her bodyguards. Her son Rajiv Gandhi was sworn in as India's youngest premier. Members of NehruGandhi family have been prime minister for approximately 38 years."]], "1_corpus": "wikipedia"}
@@ -21,4 +19,3 @@
 {"tstamp": 1722359625.838, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "cfd7524090934e63a83ff9284cc4d54c", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "Paper that improves code prediction accuracy using a syntactically-aware Transformer model.", "0_output": [["Paper that improves code prediction accuracy using a syntactically-aware Transformer model.", "Title: StructCoder: Structure-Aware Transformer for Code Generation\n\nAbstract: There has been a recent surge of interest in automating software engineering tasks using deep learning. This paper addresses the problem of code generation, where the goal is to generate target code given source code in a different language or a natural language description. Most state-of-the-art deep learning models for code generation use training strategies primarily designed for natural language. However, understanding and generating code requires a more rigorous comprehension of the code syntax and semantics. With this motivation, we develop an encoder-decoder Transformer model where both the encoder and decoder are explicitly trained to recognize the syntax and data flow in the source and target codes, respectively. We not only make the encoder structure-aware by leveraging the source code's syntax tree and data flow graph, but we also support the decoder in preserving the syntax and data flow of the target code by introducing two novel auxiliary tasks: AST (Abstract Syntax Tree) paths prediction and data flow prediction. To the best of our knowledge, this is the first work to introduce a structure-aware Transformer decoder that models both syntax and data flow to enhance the quality of generated code. The proposed StructCoder model achieves state-of-the-art performance on code translation and text-to-code generation tasks in the CodeXGLUE benchmark, and improves over baselines of similar size on the APPS code generation benchmark. Our code is publicly available at https://github.com/reddy-lab-code-research/StructCoder/."]], "0_corpus": "arxiv", "1_conv_id": "1e72b400d20445c1a5f905e301166625", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "Paper that improves code prediction accuracy using a syntactically-aware Transformer model.", "1_output": [["Paper that improves code prediction accuracy using a syntactically-aware Transformer model.", "Title: Empirical Study of Transformers for Source Code\n\nAbstract: Initially developed for natural language processing (NLP), Transformers are now widely used for source code processing, due to the format similarity between source code and text. In contrast to natural language, source code is strictly structured, i.e., it follows the syntax of the programming language. Several recent works develop Transformer modifications for capturing syntactic information in source code. The drawback of these works is that they do not compare to each other and consider different tasks. In this work, we conduct a thorough empirical study of the capabilities of Transformers to utilize syntactic information in different tasks. We consider three tasks (code completion, function naming and bug fixing) and re-implement different syntax-capturing modifications in a unified framework. We show that Transformers are able to make meaningful predictions based purely on syntactic information and underline the best practices of taking the syntactic information into account for improving the performance of the model."]], "1_corpus": "arxiv"}
 {"tstamp": 1722360183.5049, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "8e4d32f5269142e68ec26538741715ea", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": "Why is the sky blue?", "0_output": [["Why is the sky blue?", "Sky\n\nEtymology The word sky comes from the Old Norse , meaning 'cloud, abode of God'. The Norse term is also the source of the Old English , which shares the same Indo-European base as the classical Latin , meaning 'obscure'.\nIn Old English, the term heaven was used to describe the observable expanse above the earth. During the period of  Middle English, \"heaven\" began shifting toward its current, religious meaning.\nDuring daytime\nExcept for direct sunlight, most of the light in the daytime sky is caused by scattering, which is dominated by a small-particle limit called Rayleigh scattering. The scattering due to molecule-sized particles (as in air) is greater in the directions both toward and away from the source of light than it is in directions perpendicular to the incident path. Scattering is significant for light at all visible wavelengths, but is stronger at the shorter (bluer) end of the visible spectrum, meaning that the scattered light is bluer than its source: the Sun. The remaining direct sunlight, having lost some of its shorter-wavelength components, appears slightly less blue.\nScattering also occurs even more strongly in clouds. Individual water droplets refract white light into a set of colored rings. If a cloud is thick enough, scattering from multiple water droplets will wash out the set of colored rings and create a washed-out white color."]], "0_corpus": "wikipedia", "1_conv_id": "00cd735a722943bca9ff50386cee6ee3", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "Why is the sky blue?", "1_output": [["Why is the sky blue?", "Rayleigh scattering\n\nRayleigh scattering ( ), named after the 19th-century British physicist Lord Rayleigh (John William Strutt), is the predominantly elastic scattering of light, or other electromagnetic radiation, by particles with a size much smaller than the wavelength of the radiation. For light frequencies well below the resonance frequency of the scattering medium (normal dispersion regime), the amount of scattering is inversely proportional to the fourth power of the wavelength, e.g., a blue color is scattered much more than a red color as light propagates through air.\nRayleigh scattering results from the electric polarizability of the particles. The oscillating electric field of a light wave acts on the charges within a particle, causing them to move at the same frequency. The particle, therefore, becomes a small radiating dipole whose radiation we see as scattered light. The particles may be individual atoms or molecules; it can occur when light travels through transparent solids and liquids, but is most prominently seen in gases.\nRayleigh scattering of sunlight in Earth's atmosphere causes diffuse sky radiation, which is the reason for the blue color of the daytime and twilight sky, as well as the yellowish to reddish hue of the low Sun. Sunlight is also subject to Raman scattering, which changes the rotational state of the molecules and gives rise to polarization effects."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722360252.5966, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "1b169b7653ab4c8091ecb4bf186579e2", "0_model_name": "embed-english-v3.0", "0_prompt": "Are there grass or fiber eating birds?", "0_output": [["Are there grass or fiber eating birds?", "Bird\n\nDiet and feeding are varied and often include nectar, fruit, plants, seeds, carrion, and various small animals, including other birds. The digestive system of birds is unique, with a crop for storage and a gizzard that contains swallowed stones for grinding food to compensate for the lack of teeth. Some species such as pigeons and some psittacine species do not have a gallbladder. Most birds are highly adapted for rapid digestion to aid with flight. Some migratory birds have adapted to use protein stored in many parts of their bodies, including protein from the intestines, as additional energy during migration.\nBirds that employ many strategies to obtain food or feed on a variety of food items are called generalists, while others that concentrate time and effort on specific food items or have a single strategy to obtain food are considered specialists. Avian foraging strategies can vary widely by species. Many birds glean for insects, invertebrates, fruit, or seeds. Some hunt insects by suddenly attacking from a branch. Those species that seek pest insects are considered beneficial 'biological control agents' and their presence encouraged in biological pest control programmes. Combined, insectivorous birds eat 400\u2013500 million metric tons of arthropods annually.\nNectar feeders such as hummingbirds, sunbirds, lories, and lorikeets amongst others have specially adapted brushy tongues and in many cases bills designed to fit co-adapted flowers. Kiwis and shorebirds with long bills probe for invertebrates; shorebirds' varied bill lengths and feeding methods result in the separation of ecological niches. Loons, diving ducks, penguins and auks pursue their prey underwater, using their wings or feet for propulsion, while aerial predators such as sulids, kingfishers and terns plunge dive after their prey. Flamingos, three species of prion, and some ducks are filter feeders. Geese and dabbling ducks are primarily grazers."]], "0_corpus": "wikipedia", "1_conv_id": "bf7123c3bf574070873b3438c66a7145", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "Are there grass or fiber eating birds?", "1_output": [["Are there grass or fiber eating birds?", "Bird\n\nDiet and feeding are varied and often include nectar, fruit, plants, seeds, carrion, and various small animals, including other birds. The digestive system of birds is unique, with a crop for storage and a gizzard that contains swallowed stones for grinding food to compensate for the lack of teeth. Some species such as pigeons and some psittacine species do not have a gallbladder. Most birds are highly adapted for rapid digestion to aid with flight. Some migratory birds have adapted to use protein stored in many parts of their bodies, including protein from the intestines, as additional energy during migration.\nBirds that employ many strategies to obtain food or feed on a variety of food items are called generalists, while others that concentrate time and effort on specific food items or have a single strategy to obtain food are considered specialists. Avian foraging strategies can vary widely by species. Many birds glean for insects, invertebrates, fruit, or seeds. Some hunt insects by suddenly attacking from a branch. Those species that seek pest insects are considered beneficial 'biological control agents' and their presence encouraged in biological pest control programmes. Combined, insectivorous birds eat 400\u2013500 million metric tons of arthropods annually.\nNectar feeders such as hummingbirds, sunbirds, lories, and lorikeets amongst others have specially adapted brushy tongues and in many cases bills designed to fit co-adapted flowers. Kiwis and shorebirds with long bills probe for invertebrates; shorebirds' varied bill lengths and feeding methods result in the separation of ecological niches. Loons, diving ducks, penguins and auks pursue their prey underwater, using their wings or feet for propulsion, while aerial predators such as sulids, kingfishers and terns plunge dive after their prey. Flamingos, three species of prion, and some ducks are filter feeders. Geese and dabbling ducks are primarily grazers."]], "1_corpus": "wikipedia"}
-{"tstamp": 1722360320.5742, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "b454985e65da4a54967a4a82ac91170c", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": "Can plants get cancer?", "0_output": [["Can plants get cancer?", "Blue agave\n\nTequila production Tequila is made only from a specific cultivar of Agave tequilana called 'Weber Azul'. The plant is from the Asparagaceae family. This cultivar is larger and blue-gray in color compared to the smaller and green normal A.\u00a0tequilana. It is a rapid grower and prolific offsetter in keeping with its agricultural advantages. Tequila is produced by removing the heart (pi\u00f1a) of the plant in its seventh to fourteenth year (depending on growth rate and whims of harvester). Harvested pi\u00f1as normally weigh . This heart is stripped of its leaves and heated to convert the inulin to sugars. Then the roasted core is pressed or crushed to release the sugary clear liquid called aguamiel, which is, in turn, fermented and distilled into alcohol. Tequila is also made with a sugar formulation of 51% agave and 49% other sugars. These tequilas are referred to as Mixtos.\nPathogens affecting the plant\nAs agave production has moved to an industrial scale since the end of the 1980s, diseases and pests, collectively referred to as TMA (tristeza y muerte de agave, \"wilting and death of agave\"), have hit the crops. Through the 1990s, diseases spread, particularly Fusarium fungi and Erwinia bacteria, exacerbated by the low genetic diversity of the agave plants. Other problems include the agave weevil, Scyphophorus acupunctatus, and a fungus, Thielaviopsis paradoxa.\nAccording to a 2004 study, additional pathogens, Erwinia carotovora, Enterobacter agglomerans, Pseudomonas mendocina, and Serratia spp. are responsible for continued rot."]], "0_corpus": "wikipedia", "1_conv_id": "8e0a2106198b416899abf77d45576c31", "1_model_name": "voyage-multilingual-2", "1_prompt": "Can plants get cancer?", "1_output": [["Can plants get cancer?", "List of unproven and disproven cancer treatments\n\nVenus flytrap \u2013 a carnivorous plant, the extract of which is promoted as a treatment for a variety of human ailments including skin cancer. According to the American Cancer Society, \"available scientific evidence does not support claims that extract from the Venus flytrap plant is effective in treating skin cancer or any other type of cancer\".\nWalnuts \u2013 large, hard edible seeds of any tree of the genus Juglans. Black walnut has been promoted as a cancer cure on the basis it kills a \"parasite\" responsible for the disease. However, according to the American Cancer Society, \"available scientific evidence does not support claims that hulls from black walnuts remove parasites from the intestinal tract or that they are effective in treating cancer or any other disease\".\nWheatgrass \u2013 a food made from grains of wheat. According to the American Cancer Society, although some wheatgrass champions claim it can \"shrink\" cancer tumors, \"available scientific evidence does not support the idea that wheatgrass or the wheatgrass diet can cure or prevent disease\".\nWild yam (or Chinese yam) \u2013 types of yam, the roots of which are made into creams and dietary supplements that are promoted for a variety of medicinal purposes, including cancer prevention. The American Cancer Society says of these products, \"available scientific evidence does not support claims that they are safe or effective.\""]], "1_corpus": "wikipedia"}

data/retrieval_battle-f0f24c65-6f65-4435-a1e5-20bb12a2b295.jsonl

@@ -16,7 +16,6 @@
 {"tstamp": 1722224747.2038, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "27b74b6e3c64477498043a197913dbe6", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": "Unified-IO model", "0_output": [["Unified-IO model", "Title: Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision, Language, Audio, and Action\n\nAbstract: We present Unified-IO 2, the first autoregressive multimodal model that is capable of understanding and generating image, text, audio, and action. To unify different modalities, we tokenize inputs and outputs -- images, text, audio, action, bounding boxes, etc., into a shared semantic space and then process them with a single encoder-decoder transformer model. Since training with such diverse modalities is challenging, we propose various architectural improvements to stabilize model training. We train our model from scratch on a large multimodal pre-training corpus from diverse sources with a multimodal mixture of denoisers objective. To learn an expansive set of skills, such as following multimodal instructions, we construct and finetune on an ensemble of 120 datasets with prompts and augmentations. With a single unified model, Unified-IO 2 achieves state-of-the-art performance on the GRIT benchmark and strong results in more than 35 benchmarks, including image generation and understanding, natural language understanding, video and audio understanding, and robotic manipulation. We release all our models to the research community."]], "0_corpus": "arxiv", "1_conv_id": "b4bc088789164404bebf053d0828474d", "1_model_name": "embed-english-v3.0", "1_prompt": "Unified-IO model", "1_output": [["Unified-IO model", "Title: A Unified Programming Model for Heterogeneous Computing with CPU and Accelerator Technologies\n\nAbstract: This paper consists of three parts. The first part provides a unified programming model for heterogeneous computing with CPU and accelerator (like GPU, FPGA, Google TPU, Atos QPU, and more) technologies. To some extent, this new programming model makes programming across CPUs and accelerators turn into usual programming tasks with common programming languages, and relieves complexity of programming across CPUs and accelerators. It can be achieved by extending file managements in common programming languages, such as C/C++, Fortran, Python, MPI, etc., to cover accelerators as I/O devices. In the second part, we show that all types of computer systems can be reduced to the simplest type of computer system, a single-core CPU computer system with I/O devices, by the unified programming model. Thereby, the unified programming model can truly build the programming of various computer systems on one API (i.e. file managements of common programming languages), and can make programming for various computer systems easier. In third part, we present a new approach to coupled applications computing (like multidisciplinary simulations) by the unified programming model. The unified programming model makes coupled applications computing more natural and easier since it only relies on its own power to couple multiple applications through MPI."]], "1_corpus": "arxiv"}
 {"tstamp": 1722224768.3473, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "72ed621be6a54d4a8eb632a62857fa63", "0_model_name": "embed-english-v3.0", "0_prompt": "Good benchmark for multitask performance of llms", "0_output": [["Good benchmark for multitask performance of llms", "Title: Multi-Task Inference: Can Large Language Models Follow Multiple Instructions at Once?\n\nAbstract: Large language models (LLMs) are typically prompted to follow a single instruction per inference call. In this work, we analyze whether LLMs also hold the capability to handle multiple instructions simultaneously, denoted as Multi-Task Inference. For this purpose, we introduce the MTI Bench(Multi-Task Inference Benchmark), a comprehensive evaluation benchmark encompassing 5,000 instances across 25 tasks. Each task in the MTI Bench involves 2 to 3 sub-tasks. As expected, we first demonstrate that Multi-Task Inference reduces the total inference time by 1.46 times in average since it does not require multiple inference calls. Interestingly, contrary to the expectation that LLMs would perform better when tasks are divided, we find that state-of-the-art LLMs, such as Llama-2-Chat-70B and GPT-4, show up to 7.3% and 12.4% improved performance with Multi-Task Inference compared to Single-Task Inference on the MTI Bench. We release the MTI Bench dataset and our code at this link https://github.com/guijinSON/MTI-Bench."]], "0_corpus": "arxiv", "1_conv_id": "492d5f636fc94d049f457c71188d11c0", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": "Good benchmark for multitask performance of llms", "1_output": [["Good benchmark for multitask performance of llms", "Title: Limit theorems and ergodicity for general bootstrap random walks\n\nAbstract: Given the increments of a simple symmetric random walk $(X_n)_{n\\ge0}$, we characterize all possible ways of recycling these increments into a simple symmetric random walk $(Y_n)_{n\\ge0}$ adapted to the filtration of $(X_n)_{n\\ge0}$. We study the long term behavior of a suitably normalized two-dimensional process $((X_n,Y_n))_{n\\ge0}$. In particular, we provide necessary and sufficient conditions for the process to converge to a two-dimensional Brownian motion (possibly degenerate). We also discuss cases in which the limit is not Gaussian. Finally, we provide a simple necessary and sufficient condition for the ergodicity of the recycling transformation, thus generalizing results from Dubins and Smorodinsky (1992) and Fujita (2008), and solving the discrete version of the open problem of the ergodicity of the general L\\'evy transformation (see Mansuy and Yor, 2006)."]], "1_corpus": "arxiv"}
 {"tstamp": 1722224795.2981, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "00300e8adfc2436a82273eaac0616ce2", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "A paper evaluating whether updating one fact in a language model affects related facts.", "0_output": [["A paper evaluating whether updating one fact in a language model affects related facts.", "Title: Evaluating the Ripple Effects of Knowledge Editing in Language Models\n\nAbstract: Modern language models capture a large body of factual knowledge. However, some facts can be incorrectly induced or become obsolete over time, resulting in factually incorrect generations. This has led to the development of various editing methods that allow updating facts encoded by the model. Evaluation of these methods has primarily focused on testing whether an individual fact has been successfully injected, and if similar predictions for other subjects have not changed. Here we argue that such evaluation is limited, since injecting one fact (e.g. ``Jack Depp is the son of Johnny Depp'') introduces a ``ripple effect'' in the form of additional facts that the model needs to update (e.g.``Jack Depp is the sibling of Lily-Rose Depp''). To address this issue, we propose a novel set of evaluation criteria that consider the implications of an edit on related facts. Using these criteria, we then construct RippleEdits, a diagnostic benchmark of 5K factual edits, capturing a variety of types of ripple effects. We evaluate prominent editing methods on RippleEdits, showing that current methods fail to introduce consistent changes in the model's knowledge. In addition, we find that a simple in-context editing baseline obtains the best scores on our benchmark, suggesting a promising research direction for model editing."]], "0_corpus": "arxiv", "1_conv_id": "44bd3abae6e44faba2d1cf54ce8bec32", "1_model_name": "text-embedding-004", "1_prompt": "A paper evaluating whether updating one fact in a language model affects related facts.", "1_output": [["A paper evaluating whether updating one fact in a language model affects related facts.", "Title: WikiFactDiff: A Large, Realistic, and Temporally Adaptable Dataset for Atomic Factual Knowledge Update in Causal Language Models\n\nAbstract: The factuality of large language model (LLMs) tends to decay over time since events posterior to their training are \"unknown\" to them. One way to keep models up-to-date could be factual update: the task of inserting, replacing, or removing certain simple (atomic) facts within the model. To study this task, we present WikiFactDiff, a dataset that describes the evolution of factual knowledge between two dates as a collection of simple facts divided into three categories: new, obsolete, and static. We describe several update scenarios arising from various combinations of these three types of basic update. The facts are represented by subject-relation-object triples; indeed, WikiFactDiff was constructed by comparing the state of the Wikidata knowledge base at 4 January 2021 and 27 February 2023. Those fact are accompanied by verbalization templates and cloze tests that enable running update algorithms and their evaluation metrics. Contrary to other datasets, such as zsRE and CounterFact, WikiFactDiff constitutes a realistic update setting that involves various update scenarios, including replacements, archival, and new entity insertions. We also present an evaluation of existing update algorithms on WikiFactDiff."]], "1_corpus": "arxiv"}
-{"tstamp": 1722224897.7564, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "66d97eb1cd7d4e31b48bcf9e823bba43", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": "A method using descriptors from large language models to improve accuracy and mitigate bias in visual classification tasks.", "0_output": [["A method using descriptors from large language models to improve accuracy and mitigate bias in visual classification tasks.", "Title: Images in Language Space: Exploring the Suitability of Large Language Models for Vision & Language Tasks\n\nAbstract: Large language models have demonstrated robust performance on various language tasks using zero-shot or few-shot learning paradigms. While being actively researched, multimodal models that can additionally handle images as input have yet to catch up in size and generality with language-only models. In this work, we ask whether language-only models can be utilised for tasks that require visual input -- but also, as we argue, often require a strong reasoning component. Similar to some recent related work, we make visual information accessible to the language model using separate verbalisation models. Specifically, we investigate the performance of open-source, open-access language models against GPT-3 on five vision-language tasks when given textually-encoded visual information. Our results suggest that language models are effective for solving vision-language tasks even with limited samples. This approach also enhances the interpretability of a model's output by providing a means of tracing the output back through the verbalised image content."]], "0_corpus": "arxiv", "1_conv_id": "e97d1a7a80bf4651a54001a5a1acb3b0", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "A method using descriptors from large language models to improve accuracy and mitigate bias in visual classification tasks.", "1_output": [["A method using descriptors from large language models to improve accuracy and mitigate bias in visual classification tasks.", "Title: Visual Classification via Description from Large Language Models\n\nAbstract: Vision-language models (VLMs) such as CLIP have shown promising performance on a variety of recognition tasks using the standard zero-shot classification procedure -- computing similarity between the query image and the embedded words for each category. By only using the category name, they neglect to make use of the rich context of additional information that language affords. The procedure gives no intermediate understanding of why a category is chosen, and furthermore provides no mechanism for adjusting the criteria used towards this decision. We present an alternative framework for classification with VLMs, which we call classification by description. We ask VLMs to check for descriptive features rather than broad categories: to find a tiger, look for its stripes; its claws; and more. By basing decisions on these descriptors, we can provide additional cues that encourage using the features we want to be used. In the process, we can get a clear idea of what features the model uses to construct its decision; it gains some level of inherent explainability. We query large language models (e.g., GPT-3) for these descriptors to obtain them in a scalable way. Extensive experiments show our framework has numerous advantages past interpretability. We show improvements in accuracy on ImageNet across distribution shifts; demonstrate the ability to adapt VLMs to recognize concepts unseen during training; and illustrate how descriptors can be edited to effectively mitigate bias compared to the baseline."]], "1_corpus": "arxiv"}
 {"tstamp": 1722224923.4451, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "04682ef77bb14d3d987fa49503bd1c7b", "0_model_name": "voyage-multilingual-2", "0_prompt": "Highest BLEU scores for fully NAT in WMT14 En-De with faster inference.", "0_output": [["Highest BLEU scores for fully NAT in WMT14 En-De with faster inference.", "Title: Fully Non-autoregressive Neural Machine Translation: Tricks of the Trade\n\nAbstract: Fully non-autoregressive neural machine translation (NAT) is proposed to simultaneously predict tokens with single forward of neural networks, which significantly reduces the inference latency at the expense of quality drop compared to the Transformer baseline. In this work, we target on closing the performance gap while maintaining the latency advantage. We first inspect the fundamental issues of fully NAT models, and adopt dependency reduction in the learning space of output tokens as the basic guidance. Then, we revisit methods in four different aspects that have been proven effective for improving NAT models, and carefully combine these techniques with necessary modifications. Our extensive experiments on three translation benchmarks show that the proposed system achieves the new state-of-the-art results for fully NAT models, and obtains comparable performance with the autoregressive and iterative NAT systems. For instance, one of the proposed models achieves 27.49 BLEU points on WMT14 En-De with approximately 16.5X speed up at inference time."]], "0_corpus": "arxiv", "1_conv_id": "e6e5e63a277c497b87192e49b67e8463", "1_model_name": "text-embedding-004", "1_prompt": "Highest BLEU scores for fully NAT in WMT14 En-De with faster inference.", "1_output": [["Highest BLEU scores for fully NAT in WMT14 En-De with faster inference.", "Title: Fully Non-autoregressive Neural Machine Translation: Tricks of the Trade\n\nAbstract: Fully non-autoregressive neural machine translation (NAT) is proposed to simultaneously predict tokens with single forward of neural networks, which significantly reduces the inference latency at the expense of quality drop compared to the Transformer baseline. In this work, we target on closing the performance gap while maintaining the latency advantage. We first inspect the fundamental issues of fully NAT models, and adopt dependency reduction in the learning space of output tokens as the basic guidance. Then, we revisit methods in four different aspects that have been proven effective for improving NAT models, and carefully combine these techniques with necessary modifications. Our extensive experiments on three translation benchmarks show that the proposed system achieves the new state-of-the-art results for fully NAT models, and obtains comparable performance with the autoregressive and iterative NAT systems. For instance, one of the proposed models achieves 27.49 BLEU points on WMT14 En-De with approximately 16.5X speed up at inference time."]], "1_corpus": "arxiv"}
 {"tstamp": 1722224949.6115, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "f93384bd5692403b9b937e5912ee5056", "0_model_name": "voyage-multilingual-2", "0_prompt": "A comprehensive analysis of a 280 billion parameter language model and its performance across 152 diverse tasks ", "0_output": [["A comprehensive analysis of a 280 billion parameter language model and its performance across 152 diverse tasks ", "Title: Baichuan 2: Open Large-scale Language Models\n\nAbstract: Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering. However, most powerful LLMs are closed-source or limited in their capability for languages other than English. In this technical report, we present Baichuan 2, a series of large-scale multilingual language models containing 7 billion and 13 billion parameters, trained from scratch, on 2.6 trillion tokens. Baichuan 2 matches or outperforms other open-source models of similar size on public benchmarks like MMLU, CMMLU, GSM8K, and HumanEval. Furthermore, Baichuan 2 excels in vertical domains such as medicine and law. We will release all pre-training model checkpoints to benefit the research community in better understanding the training dynamics of Baichuan 2."]], "0_corpus": "arxiv", "1_conv_id": "582d39f1436e46c086fb73ddaf7c9a24", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": "A comprehensive analysis of a 280 billion parameter language model and its performance across 152 diverse tasks ", "1_output": [["A comprehensive analysis of a 280 billion parameter language model and its performance across 152 diverse tasks ", "Title: Seq2Mol: Automatic design of de novo molecules conditioned by the target protein sequences through deep neural networks\n\nAbstract: De novo design of molecules has recently enjoyed the power of generative deep neural networks. Current approaches aim to generate molecules either resembling the properties of the molecules of the training set or molecules that are optimized with respect to specific physicochemical properties. None of the methods generates molecules specific to a target protein. In the approach presented here, we introduce a method which is conditioned on the protein target sequence to generate de novo molecules that are relevant to the target. We use an implementation adapted from Google's \"Show and Tell\" image caption generation method, to generate SMILES strings of molecules from protein sequence embeddings generated by a deep bi-directional language model ELMo. ELMo is used to generate contextualized embedding vectors of the protein sequence. Using reinforcement learning, the trained model is further optimized through augmented episodic likelihood to increase the diversity of the generated compounds compared to the training set. We used the model to generate compounds for two major drug target families, i.e. for GPCRs and Tyrosine Kinase targets. The model generated compounds which are structurally different form the training set, while also being more similar to compounds known to bind to the two families of drug targets compared to a random set of molecules. The compounds further display reasonable synthesizability and drug-likeness scores."]], "1_corpus": "arxiv"}
 {"tstamp": 1722224964.6848, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "d3b69c82e28b436c969357870e392c7a", "0_model_name": "embed-english-v3.0", "0_prompt": "Paper introducing a model connecting object segmentation masks with natural language responses in multimodal settings.", "0_output": [["Paper introducing a model connecting object segmentation masks with natural language responses in multimodal settings.", "Title: Dynamic Multimodal Instance Segmentation guided by natural language queries\n\nAbstract: We address the problem of segmenting an object given a natural language expression that describes it. Current techniques tackle this task by either (\\textit{i}) directly or recursively merging linguistic and visual information in the channel dimension and then performing convolutions; or by (\\textit{ii}) mapping the expression to a space in which it can be thought of as a filter, whose response is directly related to the presence of the object at a given spatial coordinate in the image, so that a convolution can be applied to look for the object. We propose a novel method that integrates these two insights in order to fully exploit the recursive nature of language. Additionally, during the upsampling process, we take advantage of the intermediate information generated when downsampling the image, so that detailed segmentations can be obtained. We compare our method against the state-of-the-art approaches in four standard datasets, in which it surpasses all previous methods in six of eight of the splits for this task."]], "0_corpus": "arxiv", "1_conv_id": "b353792ca18b4bc9967223f30e5f0dc0", "1_model_name": "voyage-multilingual-2", "1_prompt": "Paper introducing a model connecting object segmentation masks with natural language responses in multimodal settings.", "1_output": [["Paper introducing a model connecting object segmentation masks with natural language responses in multimodal settings.", "Title: Comprehensive Multi-Modal Interactions for Referring Image Segmentation\n\nAbstract: We investigate Referring Image Segmentation (RIS), which outputs a segmentation map corresponding to the natural language description. Addressing RIS efficiently requires considering the interactions happening across visual and linguistic modalities and the interactions within each modality. Existing methods are limited because they either compute different forms of interactions sequentially (leading to error propagation) or ignore intramodal interactions. We address this limitation by performing all three interactions simultaneously through a Synchronous Multi-Modal Fusion Module (SFM). Moreover, to produce refined segmentation masks, we propose a novel Hierarchical Cross-Modal Aggregation Module (HCAM), where linguistic features facilitate the exchange of contextual information across the visual hierarchy. We present thorough ablation studies and validate our approach's performance on four benchmark datasets, showing considerable performance gains over the existing state-of-the-art (SOTA) methods."]], "1_corpus": "arxiv"}
@@ -25,7 +24,6 @@
 {"tstamp": 1722225041.8999, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "e4e6ff5dc3a149a8a9cd5bc76feefd62", "0_model_name": "Salesforce/SFR-Embedding-2_R", "0_prompt": "Looking for the paper on the 2020 CLEF edition covering automatic claim verification in social media.", "0_output": [["Looking for the paper on the 2020 CLEF edition covering automatic claim verification in social media.", "Title: Overview of CheckThat! 2020: Automatic Identification and Verification of Claims in Social Media\n\nAbstract: We present an overview of the third edition of the CheckThat! Lab at CLEF 2020. The lab featured five tasks in two different languages: English and Arabic. The first four tasks compose the full pipeline of claim verification in social media: Task 1 on check-worthiness estimation, Task 2 on retrieving previously fact-checked claims, Task 3 on evidence retrieval, and Task 4 on claim verification. The lab is completed with Task 5 on check-worthiness estimation in political debates and speeches. A total of 67 teams registered to participate in the lab (up from 47 at CLEF 2019), and 23 of them actually submitted runs (compared to 14 at CLEF 2019). Most teams used deep neural networks based on BERT, LSTMs, or CNNs, and achieved sizable improvements over the baselines on all tasks. Here we describe the tasks setup, the evaluation results, and a summary of the approaches used by the participants, and we discuss some lessons learned. Last but not least, we release to the research community all datasets from the lab as well as the evaluation scripts, which should enable further research in the important tasks of check-worthiness estimation and automatic claim verification."]], "0_corpus": "arxiv", "1_conv_id": "069ed0d8c0fe49369e9c70a31c707261", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_prompt": "Looking for the paper on the 2020 CLEF edition covering automatic claim verification in social media.", "1_output": [["Looking for the paper on the 2020 CLEF edition covering automatic claim verification in social media.", "Title: Overview of CheckThat! 2020: Automatic Identification and Verification of Claims in Social Media\n\nAbstract: We present an overview of the third edition of the CheckThat! Lab at CLEF 2020. The lab featured five tasks in two different languages: English and Arabic. The first four tasks compose the full pipeline of claim verification in social media: Task 1 on check-worthiness estimation, Task 2 on retrieving previously fact-checked claims, Task 3 on evidence retrieval, and Task 4 on claim verification. The lab is completed with Task 5 on check-worthiness estimation in political debates and speeches. A total of 67 teams registered to participate in the lab (up from 47 at CLEF 2019), and 23 of them actually submitted runs (compared to 14 at CLEF 2019). Most teams used deep neural networks based on BERT, LSTMs, or CNNs, and achieved sizable improvements over the baselines on all tasks. Here we describe the tasks setup, the evaluation results, and a summary of the approaches used by the participants, and we discuss some lessons learned. Last but not least, we release to the research community all datasets from the lab as well as the evaluation scripts, which should enable further research in the important tasks of check-worthiness estimation and automatic claim verification."]], "1_corpus": "arxiv"}
 {"tstamp": 1722225064.1705, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "ad3321dd65264d9ea93d600f7971ff6d", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": "can r squared adjusted be negative?", "0_output": [["can r squared adjusted be negative?", "Stats Stackexchange\n\nQ: Is it a problem to get a negative adjusted r-squared? Background:\nI have the cross-sectional model:\n$Y_{i}  = a + b X_{1,i} + c X_{2,i} + d X_{3,i} + e X_{4,i} + \\nu_i$.\nThe application is corporate finance. So each $Y_i$ is something like the change in return on assets over a 1 year period for firm $i$, and the regressors are typical corporate finance variables.\nIn corporate finance, very small values of $R^2$ are common, even sometimes $1\\%$. My $R^2$ is around $1\\%$ but my Adjusted $R^2$ is $-0.2\\%$. \nI have never seen papers report negative Adjusted $R^2$ but this could just be because they omit the publication of their adjusted $R^2$ when they see that it is negative. \nQuestion\nIs there some problem when the adjusted $R^2$ is negative?\n\nA: The formula for adjusted R square allows it to be negative.  It is intended to approximate the actual percentage variance explained.  So if the actual R square is close to zero the adjusted R square can be slightly negative.  Just think of it as an estimate of zero.\n"]], "0_corpus": "stackexchange", "1_conv_id": "6766409719cd420188c069799f6c48d1", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "can r squared adjusted be negative?", "1_output": [["can r squared adjusted be negative?", "Stats Stackexchange\n\nQ: Is it a problem to get a negative adjusted r-squared? Background:\nI have the cross-sectional model:\n$Y_{i}  = a + b X_{1,i} + c X_{2,i} + d X_{3,i} + e X_{4,i} + \\nu_i$.\nThe application is corporate finance. So each $Y_i$ is something like the change in return on assets over a 1 year period for firm $i$, and the regressors are typical corporate finance variables.\nIn corporate finance, very small values of $R^2$ are common, even sometimes $1\\%$. My $R^2$ is around $1\\%$ but my Adjusted $R^2$ is $-0.2\\%$. \nI have never seen papers report negative Adjusted $R^2$ but this could just be because they omit the publication of their adjusted $R^2$ when they see that it is negative. \nQuestion\nIs there some problem when the adjusted $R^2$ is negative?\n\nA: The formula for adjusted R square allows it to be negative.  It is intended to approximate the actual percentage variance explained.  So if the actual R square is close to zero the adjusted R square can be slightly negative.  Just think of it as an estimate of zero.\n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1722225082.4068, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "fe65d33c55ca4dbf856b34d9fd465f40", "0_model_name": "text-embedding-3-large", "0_prompt": "Paper showcasing reinforcement learning from human feedback to train beneficial and safe AI assistants.", "0_output": [["Paper showcasing reinforcement learning from human feedback to train beneficial and safe AI assistants.", "Title: Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback\n\nAbstract: We apply preference modeling and reinforcement learning from human feedback (RLHF) to finetune language models to act as helpful and harmless assistants. We find this alignment training improves performance on almost all NLP evaluations, and is fully compatible with training for specialized skills such as python coding and summarization. We explore an iterated online mode of training, where preference models and RL policies are updated on a weekly cadence with fresh human feedback data, efficiently improving our datasets and models. Finally, we investigate the robustness of RLHF training, and identify a roughly linear relation between the RL reward and the square root of the KL divergence between the policy and its initialization. Alongside our main results, we perform peripheral analyses on calibration, competing objectives, and the use of OOD detection, compare our models with human writers, and provide samples from our models using prompts appearing in recent related work."]], "0_corpus": "arxiv", "1_conv_id": "19f1b179abd4428983f12ecbb76ba96e", "1_model_name": "intfloat/multilingual-e5-large-instruct", "1_prompt": "Paper showcasing reinforcement learning from human feedback to train beneficial and safe AI assistants.", "1_output": [["Paper showcasing reinforcement learning from human feedback to train beneficial and safe AI assistants.", "Title: Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback\n\nAbstract: We apply preference modeling and reinforcement learning from human feedback (RLHF) to finetune language models to act as helpful and harmless assistants. We find this alignment training improves performance on almost all NLP evaluations, and is fully compatible with training for specialized skills such as python coding and summarization. We explore an iterated online mode of training, where preference models and RL policies are updated on a weekly cadence with fresh human feedback data, efficiently improving our datasets and models. Finally, we investigate the robustness of RLHF training, and identify a roughly linear relation between the RL reward and the square root of the KL divergence between the policy and its initialization. Alongside our main results, we perform peripheral analyses on calibration, competing objectives, and the use of OOD detection, compare our models with human writers, and provide samples from our models using prompts appearing in recent related work."]], "1_corpus": "arxiv"}
-{"tstamp": 1722225101.4447, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "94d522a8ae794130b5549c97be9efcc8", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": "Looking for a paper on Fine-grained Entity Type Classification using a neural network with hierarchy-aware loss.", "0_output": [["Looking for a paper on Fine-grained Entity Type Classification using a neural network with hierarchy-aware loss.", "Title: Graph Neural Network Approach to Semantic Type Detection in Tables\n\nAbstract: This study addresses the challenge of detecting semantic column types in relational tables, a key task in many real-world applications. While language models like BERT have improved prediction accuracy, their token input constraints limit the simultaneous processing of intra-table and inter-table information. We propose a novel approach using Graph Neural Networks (GNNs) to model intra-table dependencies, allowing language models to focus on inter-table information. Our proposed method not only outperforms existing state-of-the-art algorithms but also offers novel insights into the utility and functionality of various GNN types for semantic type detection. The code is available at https://github.com/hoseinzadeehsan/GAIT"]], "0_corpus": "arxiv", "1_conv_id": "5bc98453ebd94dd8adc5a3a7477560d1", "1_model_name": "GritLM/GritLM-7B", "1_prompt": "Looking for a paper on Fine-grained Entity Type Classification using a neural network with hierarchy-aware loss.", "1_output": [["Looking for a paper on Fine-grained Entity Type Classification using a neural network with hierarchy-aware loss.", "Title: Neural Fine-Grained Entity Type Classification with Hierarchy-Aware Loss\n\nAbstract: The task of Fine-grained Entity Type Classification (FETC) consists of assigning types from a hierarchy to entity mentions in text. Existing methods rely on distant supervision and are thus susceptible to noisy labels that can be out-of-context or overly-specific for the training sentence. Previous methods that attempt to address these issues do so with heuristics or with the help of hand-crafted features. Instead, we propose an end-to-end solution with a neural network model that uses a variant of cross- entropy loss function to handle out-of-context labels, and hierarchical loss normalization to cope with overly-specific ones. Also, previous work solve FETC a multi-label classification followed by ad-hoc post-processing. In contrast, our solution is more elegant: we use public word embeddings to train a single-label that jointly learns representations for entity mentions and their context. We show experimentally that our approach is robust against noise and consistently outperforms the state-of-the-art on established benchmarks for the task."]], "1_corpus": "arxiv"}
 {"tstamp": 1722225126.1987, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "61042e3396c64d919604031cbf9612dc", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": "A paper introducing a new large language model", "0_output": [["A paper introducing a new large language model", "Title: Larger-Context Language Modelling\n\nAbstract: In this work, we propose a novel method to incorporate corpus-level discourse information into language modelling. We call this larger-context language model. We introduce a late fusion approach to a recurrent language model based on long short-term memory units (LSTM), which helps the LSTM unit keep intra-sentence dependencies and inter-sentence dependencies separate from each other. Through the evaluation on three corpora (IMDB, BBC, and PennTree Bank), we demon- strate that the proposed model improves perplexity significantly. In the experi- ments, we evaluate the proposed approach while varying the number of context sentences and observe that the proposed late fusion is superior to the usual way of incorporating additional inputs to the LSTM. By analyzing the trained larger- context language model, we discover that content words, including nouns, adjec- tives and verbs, benefit most from an increasing number of context sentences. This analysis suggests that larger-context language model improves the unconditional language model by capturing the theme of a document better and more easily."]], "0_corpus": "arxiv", "1_conv_id": "d2e0f34f14254329b828836aa5ea813e", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "A paper introducing a new large language model", "1_output": [["A paper introducing a new large language model", "Title: Large Language Models: A Survey\n\nAbstract: Large Language Models (LLMs) have drawn a lot of attention due to their strong performance on a wide range of natural language tasks, since the release of ChatGPT in November 2022. LLMs' ability of general-purpose language understanding and generation is acquired by training billions of model's parameters on massive amounts of text data, as predicted by scaling laws \\cite{kaplan2020scaling,hoffmann2022training}. The research area of LLMs, while very recent, is evolving rapidly in many different ways. In this paper, we review some of the most prominent LLMs, including three popular LLM families (GPT, LLaMA, PaLM), and discuss their characteristics, contributions and limitations. We also give an overview of techniques developed to build, and augment LLMs. We then survey popular datasets prepared for LLM training, fine-tuning, and evaluation, review widely used LLM evaluation metrics, and compare the performance of several popular LLMs on a set of representative benchmarks. Finally, we conclude the paper by discussing open challenges and future research directions."]], "1_corpus": "arxiv"}
 {"tstamp": 1722226479.3615, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "fbd8892a82924f81812aa7a1bf175969", "0_model_name": "embed-english-v3.0", "0_prompt": "Best practices and taxonomy of data selection for efficient language model pre-training.", "0_output": [["Best practices and taxonomy of data selection for efficient language model pre-training.", "Title: How to Train Data-Efficient LLMs\n\nAbstract: The training of large language models (LLMs) is expensive. In this paper, we study data-efficient approaches for pre-training LLMs, i.e., techniques that aim to optimize the Pareto frontier of model quality and training resource/data consumption. We seek to understand the tradeoffs associated with data selection routines based on (i) expensive-to-compute data-quality estimates, and (ii) maximization of coverage and diversity-based measures in the feature space. Our first technique, Ask-LLM, leverages the zero-shot reasoning capabilities of instruction-tuned LLMs to directly assess the quality of a training example. To target coverage, we propose Density sampling, which models the data distribution to select a diverse sample. In our comparison of 19 samplers, involving hundreds of evaluation tasks and pre-training runs, we find that Ask-LLM and Density are the best methods in their respective categories. Coverage sampling can recover the performance of the full data, while models trained on Ask-LLM data consistently outperform full-data training -- even when we reject 90% of the original dataset, while converging up to 70% faster."]], "0_corpus": "arxiv", "1_conv_id": "a40e5e6bd02d47a6bdc8261d2645d034", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "Best practices and taxonomy of data selection for efficient language model pre-training.", "1_output": [["Best practices and taxonomy of data selection for efficient language model pre-training.", "Title: A Survey on Data Selection for Language Models\n\nAbstract: A major factor in the recent success of large language models is the use of enormous and ever-growing text datasets for unsupervised pre-training. However, naively training a model on all available data may not be optimal (or feasible), as the quality of available text data can vary. Filtering out data can also decrease the carbon footprint and financial costs of training models by reducing the amount of training required. Data selection methods aim to determine which candidate data points to include in the training dataset and how to appropriately sample from the selected data points. The promise of improved data selection methods has caused the volume of research in the area to rapidly expand. However, because deep learning is mostly driven by empirical evidence and experimentation on large-scale data is expensive, few organizations have the resources for extensive data selection research. Consequently, knowledge of effective data selection practices has become concentrated within a few organizations, many of which do not openly share their findings and methodologies. To narrow this gap in knowledge, we present a comprehensive review of existing literature on data selection methods and related research areas, providing a taxonomy of existing approaches. By describing the current landscape of research, this work aims to accelerate progress in data selection by establishing an entry point for new and established researchers. Additionally, throughout this review we draw attention to noticeable holes in the literature and conclude the paper by proposing promising avenues for future research."]], "1_corpus": "arxiv"}
 {"tstamp": 1722226493.0887, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "64fcb06267ce4417bdbb737a15e252f1", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": "Paper detailing advancements in self-supervised learning with ResNets that outperforms supervised benchmarks using ReLICv2.", "0_output": [["Paper detailing advancements in self-supervised learning with ResNets that outperforms supervised benchmarks using ReLICv2.", "Title: Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet?\n\nAbstract: Despite recent progress made by self-supervised methods in representation learning with residual networks, they still underperform supervised learning on the ImageNet classification benchmark, limiting their applicability in performance-critical settings. Building on prior theoretical insights from ReLIC [Mitrovic et al., 2021], we include additional inductive biases into self-supervised learning. We propose a new self-supervised representation learning method, ReLICv2, which combines an explicit invariance loss with a contrastive objective over a varied set of appropriately constructed data views to avoid learning spurious correlations and obtain more informative representations. ReLICv2 achieves $77.1\\%$ top-$1$ accuracy on ImageNet under linear evaluation on a ResNet50, thus improving the previous state-of-the-art by absolute $+1.5\\%$; on larger ResNet models, ReLICv2 achieves up to $80.6\\%$ outperforming previous self-supervised approaches with margins up to $+2.3\\%$. Most notably, ReLICv2 is the first unsupervised representation learning method to consistently outperform the supervised baseline in a like-for-like comparison over a range of ResNet architectures. Using ReLICv2, we also learn more robust and transferable representations that generalize better out-of-distribution than previous work, both on image classification and semantic segmentation. Finally, we show that despite using ResNet encoders, ReLICv2 is comparable to state-of-the-art self-supervised vision transformers."]], "0_corpus": "arxiv", "1_conv_id": "febd99e4bd594450acbc82b751f287f9", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": "Paper detailing advancements in self-supervised learning with ResNets that outperforms supervised benchmarks using ReLICv2.", "1_output": [["Paper detailing advancements in self-supervised learning with ResNets that outperforms supervised benchmarks using ReLICv2.", "Title: A Recharge Oscillator Model for Interannual Variability in Venus' Clouds\n\nAbstract: Sulfur dioxide is a radiatively and chemically important trace gas in the atmosphere of Venus and its abundance at the cloud-tops has been observed to vary on interannual to decadal timescales. This variability is thought to come from changes in the strength of convection which transports sulfur dioxide to the cloud-tops, {although} the dynamics behind such convective variability are unknown. Here we propose a new conceptual model for convective variability that links the radiative effects of water abundance at the cloud-base to convective strength within the clouds, which in turn affects water transport within the cloud. The model consists of two coupled equations which are identified as a recharge-discharge oscillator. The solutions of the coupled equations are finite amplitude sustained oscillations in convective strength and cloud-base water abundance on 3-9 year timescales. The characteristic oscillation timescale is given by the geometric mean of the radiative cooling time and the eddy mixing time near the base of the convective clouds."]], "1_corpus": "arxiv"}
@@ -40,15 +38,12 @@
 {"tstamp": 1722226790.9489, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "d8ccfaccb72b40429980442180c503b9", "0_model_name": "text-embedding-3-large", "0_prompt": "Paper introducing a transformer-based model trained on multiple visual modalities achieving high accuracy.", "0_output": [["Paper introducing a transformer-based model trained on multiple visual modalities achieving high accuracy.", "Title: Omnivore: A Single Model for Many Visual Modalities\n\nAbstract: Prior work has studied different visual modalities in isolation and developed separate architectures for recognition of images, videos, and 3D data. Instead, in this paper, we propose a single model which excels at classifying images, videos, and single-view 3D data using exactly the same model parameters. Our 'Omnivore' model leverages the flexibility of transformer-based architectures and is trained jointly on classification tasks from different modalities. Omnivore is simple to train, uses off-the-shelf standard datasets, and performs at-par or better than modality-specific models of the same size. A single Omnivore model obtains 86.0% on ImageNet, 84.1% on Kinetics, and 67.1% on SUN RGB-D. After finetuning, our models outperform prior work on a variety of vision tasks and generalize across modalities. Omnivore's shared visual representation naturally enables cross-modal recognition without access to correspondences between modalities. We hope our results motivate researchers to model visual modalities together."]], "0_corpus": "arxiv", "1_conv_id": "ba7ddb3950104cf488c87a1656f8f414", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_prompt": "Paper introducing a transformer-based model trained on multiple visual modalities achieving high accuracy.", "1_output": [["Paper introducing a transformer-based model trained on multiple visual modalities achieving high accuracy.", "Title: Ab initio Molecular Dynamics Study of D_2 Desorption from Si(100)\n\nAbstract: Ab initio molecular dynamics calculations of deuterium desorbing from Si(100) have been performed in order to monitor the energy redistribution among the hydrogen and silicon degrees of freedom during the desorption process. The calculations show that part of the potential energy at the transition state to desorption is transferred to the silicon lattice. The deuterium molecules leave the surface vibrationally hot and rotationally cold, in agreement with experiments; the mean kinetic energy, however, is larger than found in experiments."]], "1_corpus": "arxiv"}
 {"tstamp": 1722226844.4942, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "0ecf1137eb8441729be550b264620830", "0_model_name": "text-embedding-004", "0_prompt": "A novel network combining convolution and transformer operations for better medical image segmentation.", "0_output": [["A novel network combining convolution and transformer operations for better medical image segmentation.", "Title: ConvFormer: Combining CNN and Transformer for Medical Image Segmentation\n\nAbstract: Convolutional neural network (CNN) based methods have achieved great successes in medical image segmentation, but their capability to learn global representations is still limited due to using small effective receptive fields of convolution operations. Transformer based methods are capable of modelling long-range dependencies of information for capturing global representations, yet their ability to model local context is lacking. Integrating CNN and Transformer to learn both local and global representations while exploring multi-scale features is instrumental in further improving medical image segmentation. In this paper, we propose a hierarchical CNN and Transformer hybrid architecture, called ConvFormer, for medical image segmentation. ConvFormer is based on several simple yet effective designs. (1) A feed forward module of Deformable Transformer (DeTrans) is re-designed to introduce local information, called Enhanced DeTrans. (2) A residual-shaped hybrid stem based on a combination of convolutions and Enhanced DeTrans is developed to capture both local and global representations to enhance representation ability. (3) Our encoder utilizes the residual-shaped hybrid stem in a hierarchical manner to generate feature maps in different scales, and an additional Enhanced DeTrans encoder with residual connections is built to exploit multi-scale features with feature maps of different scales as input. Experiments on several datasets show that our ConvFormer, trained from scratch, outperforms various CNN- or Transformer-based architectures, achieving state-of-the-art performance."]], "0_corpus": "arxiv", "1_conv_id": "d02461de08aa4ce7b1a4cee2f252e2eb", "1_model_name": "embed-english-v3.0", "1_prompt": "A novel network combining convolution and transformer operations for better medical image segmentation.", "1_output": [["A novel network combining convolution and transformer operations for better medical image segmentation.", "Title: Rethinking Boundary Detection in Deep Learning Models for Medical Image Segmentation\n\nAbstract: Medical image segmentation is a fundamental task in the community of medical image analysis. In this paper, a novel network architecture, referred to as Convolution, Transformer, and Operator (CTO), is proposed. CTO employs a combination of Convolutional Neural Networks (CNNs), Vision Transformer (ViT), and an explicit boundary detection operator to achieve high recognition accuracy while maintaining an optimal balance between accuracy and efficiency. The proposed CTO follows the standard encoder-decoder segmentation paradigm, where the encoder network incorporates a popular CNN backbone for capturing local semantic information, and a lightweight ViT assistant for integrating long-range dependencies. To enhance the learning capacity on boundary, a boundary-guided decoder network is proposed that uses a boundary mask obtained from a dedicated boundary detection operator as explicit supervision to guide the decoding learning process. The performance of the proposed method is evaluated on six challenging medical image segmentation datasets, demonstrating that CTO achieves state-of-the-art accuracy with a competitive model complexity."]], "1_corpus": "arxiv"}
 {"tstamp": 1722226863.8341, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "3b181c53b714491a82ac48e1a1950309", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": "How do different constraints in storytelling tasks impact the author's linguistic style?", "0_output": [["How do different constraints in storytelling tasks impact the author's linguistic style?", "Title: The Effect of Different Writing Tasks on Linguistic Style: A Case Study of the ROC Story Cloze Task\n\nAbstract: A writer's style depends not just on personal traits but also on her intent and mental state. In this paper, we show how variants of the same writing task can lead to measurable differences in writing style. We present a case study based on the story cloze task (Mostafazadeh et al., 2016a), where annotators were assigned similar writing tasks with different constraints: (1) writing an entire story, (2) adding a story ending for a given story context, and (3) adding an incoherent ending to a story. We show that a simple linear classifier informed by stylistic features is able to successfully distinguish among the three cases, without even looking at the story context. In addition, combining our stylistic features with language model predictions reaches state of the art performance on the story cloze challenge. Our results demonstrate that different task framings can dramatically affect the way people write."]], "0_corpus": "arxiv", "1_conv_id": "de6bb332c59b4774b8c38bdad9af80a0", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": "How do different constraints in storytelling tasks impact the author's linguistic style?", "1_output": [["How do different constraints in storytelling tasks impact the author's linguistic style?", "Title: Limits on dynamically generated spin-orbit coupling: Absence of $l=1$ Pomeranchuk instabilities in metals\n\nAbstract: An ordered state in the spin sector that breaks parity without breaking time-reversal symmetry, i.e., that can be considered as dynamically generated spin-orbit coupling, was proposed to explain puzzling observations in a range of different systems. Here we derive severe restrictions for such a state that follow from a Ward identity related to spin conservation. It is shown that $l=1$ spin-Pomeranchuk instabilities are not possible in non-relativistic systems since the response of spin-current fluctuations is entirely incoherent and non-singular. This rules out relativistic spin-orbit coupling as an emergent low-energy phenomenon. We illustrate the exotic physical properties of the remaining higher angular momentum analogues of spin-orbit coupling and derive a geometric constraint for spin-orbit vectors in lattice systems."]], "1_corpus": "arxiv"}
-{"tstamp": 1722226877.0152, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "85881b7aeaa44439a7c415dcfd68c525", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "Study on reducing gender bias in word-level models, showing improved results in bias evaluation metrics.", "0_output": [["Study on reducing gender bias in word-level models, showing improved results in bias evaluation metrics.", "Title: Chaos or Noise - Difficulties of a Distinction\n\nAbstract: In experiments, the dynamical behavior of systems is reflected in time series. Due to the finiteness of the observational data set it is not possible to reconstruct the invariant measure up to arbitrary fine resolution and arbitrary high embedding dimension. These restrictions limit our ability to distinguish between signals generated by different systems, such as regular, chaotic or stochastic ones, when analyzed from a time series point of view. We propose to classify the signal behavior, without referring to any specific model, as stochastic or deterministic on a certain scale of the resolution $\\epsilon$, according to the dependence of the $(\\epsilon,\\tau)$-entropy, $h(\\epsilon, \\tau)$, and of the finite size Lyapunov exponent, $\\lambda(\\epsilon)$, on $\\epsilon$."]], "0_corpus": "arxiv", "1_conv_id": "a7b2c40c9e5c43e3a3e400d04100c725", "1_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "1_prompt": "Study on reducing gender bias in word-level models, showing improved results in bias evaluation metrics.", "1_output": [["Study on reducing gender bias in word-level models, showing improved results in bias evaluation metrics.", "Title: Reducing Gender Bias in Word-Level Language Models with a Gender-Equalizing Loss Function\n\nAbstract: Gender bias exists in natural language datasets which neural language models tend to learn, resulting in biased text generation. In this research, we propose a debiasing approach based on the loss function modification. We introduce a new term to the loss function which attempts to equalize the probabilities of male and female words in the output. Using an array of bias evaluation metrics, we provide empirical evidence that our approach successfully mitigates gender bias in language models without increasing perplexity. In comparison to existing debiasing strategies, data augmentation, and word embedding debiasing, our method performs better in several aspects, especially in reducing gender bias in occupation words. Finally, we introduce a combination of data augmentation and our approach, and show that it outperforms existing strategies in all bias evaluation metrics."]], "1_corpus": "arxiv"}
 {"tstamp": 1722226892.8444, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "4987ca9238374025ae9f6d61145d0142", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "Study analyzing the multitask accuracy of text models including GPT-3 on academic and professional knowledge areas.", "0_output": [["Study analyzing the multitask accuracy of text models including GPT-3 on academic and professional knowledge areas.", "Title: Vibrational Spectra of Defects in Silicon: An Orbital Radii Approach\n\nAbstract: A phenomenological approach to the stretching mode vibrational frequencies of defects in semiconductors is proposed. A novel quantum scale is defined in terms of the first principles pseudopotential based orbital radius and the principal quantum number of the element concerned. A universal linear relationship between the Sanderson electronegativity and this quantum scale is established. Next, we show that the stretching mode vibrational frequencies of hydrogen and chlorine in the silicon network scale linearly with this quantum scale. Predictions and identifications of defect environments around the Si-H and Si-Cl are possible. The assignments of vibrational modes in porous silicon are critically examined. We discuss our proposed scale in the context of Mendeleveyan scales in general, and suggest justifications for it. We believe that our approach can be gainfully extended to the vibrational spectra of other semiconductors."]], "0_corpus": "arxiv", "1_conv_id": "bf81fa11eb3f4d3cb9c7294f31d17a63", "1_model_name": "GritLM/GritLM-7B", "1_prompt": "Study analyzing the multitask accuracy of text models including GPT-3 on academic and professional knowledge areas.", "1_output": [["Study analyzing the multitask accuracy of text models including GPT-3 on academic and professional knowledge areas.", "Title: Measuring Massive Multitask Language Understanding\n\nAbstract: We propose a new test to measure a text model's multitask accuracy. The test covers 57 tasks including elementary mathematics, US history, computer science, law, and more. To attain high accuracy on this test, models must possess extensive world knowledge and problem solving ability. We find that while most recent models have near random-chance accuracy, the very largest GPT-3 model improves over random chance by almost 20 percentage points on average. However, on every one of the 57 tasks, the best models still need substantial improvements before they can reach expert-level accuracy. Models also have lopsided performance and frequently do not know when they are wrong. Worse, they still have near-random accuracy on some socially important subjects such as morality and law. By comprehensively evaluating the breadth and depth of a model's academic and professional understanding, our test can be used to analyze models across many tasks and to identify important shortcomings."]], "1_corpus": "arxiv"}
-{"tstamp": 1722226904.182, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "35ba5141430e439182b4cb93495f60b2", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": "Comparison of sparse attention and hierarchical encoding in long document transformers", "0_output": [["Comparison of sparse attention and hierarchical encoding in long document transformers", "Title: Revisiting Transformer-based Models for Long Document Classification\n\nAbstract: The recent literature in text classification is biased towards short text sequences (e.g., sentences or paragraphs). In real-world applications, multi-page multi-paragraph documents are common and they cannot be efficiently encoded by vanilla Transformer-based models. We compare different Transformer-based Long Document Classification (TrLDC) approaches that aim to mitigate the computational overhead of vanilla transformers to encode much longer text, namely sparse attention and hierarchical encoding methods. We examine several aspects of sparse attention (e.g., size of local attention window, use of global attention) and hierarchical (e.g., document splitting strategy) transformers on four document classification datasets covering different domains. We observe a clear benefit from being able to process longer text, and, based on our results, we derive practical advice of applying Transformer-based models on long document classification tasks."]], "0_corpus": "arxiv", "1_conv_id": "7c53ae4004484956a4ab85f088046698", "1_model_name": "GritLM/GritLM-7B", "1_prompt": "Comparison of sparse attention and hierarchical encoding in long document transformers", "1_output": [["Comparison of sparse attention and hierarchical encoding in long document transformers", "Title: Revisiting Transformer-based Models for Long Document Classification\n\nAbstract: The recent literature in text classification is biased towards short text sequences (e.g., sentences or paragraphs). In real-world applications, multi-page multi-paragraph documents are common and they cannot be efficiently encoded by vanilla Transformer-based models. We compare different Transformer-based Long Document Classification (TrLDC) approaches that aim to mitigate the computational overhead of vanilla transformers to encode much longer text, namely sparse attention and hierarchical encoding methods. We examine several aspects of sparse attention (e.g., size of local attention window, use of global attention) and hierarchical (e.g., document splitting strategy) transformers on four document classification datasets covering different domains. We observe a clear benefit from being able to process longer text, and, based on our results, we derive practical advice of applying Transformer-based models on long document classification tasks."]], "1_corpus": "arxiv"}
 {"tstamp": 1722226982.36, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "c81179613507466f9501f236a8beb4a9", "0_model_name": "BM25", "0_prompt": "Paper introducing universal adversarial input triggers to analyze vulnerabilities in NLP models and their transferability across different models.", "0_output": [["Paper introducing universal adversarial input triggers to analyze vulnerabilities in NLP models and their transferability across different models.", "Title: Universal Adversarial Triggers for Attacking and Analyzing NLP\n\nAbstract: Adversarial examples highlight model vulnerabilities and are useful for evaluation and interpretation. We define universal adversarial triggers: input-agnostic sequences of tokens that trigger a model to produce a specific prediction when concatenated to any input from a dataset. We propose a gradient-guided search over tokens which finds short trigger sequences (e.g., one word for classification and four words for language modeling) that successfully trigger the target prediction. For example, triggers cause SNLI entailment accuracy to drop from 89.94% to 0.55%, 72% of \"why\" questions in SQuAD to be answered \"to kill american people\", and the GPT-2 language model to spew racist output even when conditioned on non-racial contexts. Furthermore, although the triggers are optimized using white-box access to a specific model, they transfer to other models for all tasks we consider. Finally, since triggers are input-agnostic, they provide an analysis of global model behavior. For instance, they confirm that SNLI models exploit dataset biases and help to diagnose heuristics learned by reading comprehension models."]], "0_corpus": "arxiv", "1_conv_id": "95105e1e8df945a7b819bd50bcd0a76a", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "Paper introducing universal adversarial input triggers to analyze vulnerabilities in NLP models and their transferability across different models.", "1_output": [["Paper introducing universal adversarial input triggers to analyze vulnerabilities in NLP models and their transferability across different models.", "Title: Universal Adversarial Triggers for Attacking and Analyzing NLP\n\nAbstract: Adversarial examples highlight model vulnerabilities and are useful for evaluation and interpretation. We define universal adversarial triggers: input-agnostic sequences of tokens that trigger a model to produce a specific prediction when concatenated to any input from a dataset. We propose a gradient-guided search over tokens which finds short trigger sequences (e.g., one word for classification and four words for language modeling) that successfully trigger the target prediction. For example, triggers cause SNLI entailment accuracy to drop from 89.94% to 0.55%, 72% of \"why\" questions in SQuAD to be answered \"to kill american people\", and the GPT-2 language model to spew racist output even when conditioned on non-racial contexts. Furthermore, although the triggers are optimized using white-box access to a specific model, they transfer to other models for all tasks we consider. Finally, since triggers are input-agnostic, they provide an analysis of global model behavior. For instance, they confirm that SNLI models exploit dataset biases and help to diagnose heuristics learned by reading comprehension models."]], "1_corpus": "arxiv"}
 {"tstamp": 1722227007.968, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "73fda287fa15475a92a663d6a3dba7cb", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": "Achieving high accuracy in English-French plagiarism detection using word embeddings", "0_output": [["Achieving high accuracy in English-French plagiarism detection using word embeddings", "Title: UsingWord Embedding for Cross-Language Plagiarism Detection\n\nAbstract: This paper proposes to use distributed representation of words (word embeddings) in cross-language textual similarity detection. The main contributions of this paper are the following: (a) we introduce new cross-language similarity detection methods based on distributed representation of words; (b) we combine the different methods proposed to verify their complementarity and finally obtain an overall F1 score of 89.15% for English-French similarity detection at chunk level (88.5% at sentence level) on a very challenging corpus."]], "0_corpus": "arxiv", "1_conv_id": "339520347d484e1c8068e44e4e4e7452", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_prompt": "Achieving high accuracy in English-French plagiarism detection using word embeddings", "1_output": [["Achieving high accuracy in English-French plagiarism detection using word embeddings", "Title: Studies of Plasma Detachment Using a One Dimensional Model for Divertor Operation\n\nAbstract: To characterize the conditions required to reach advanced divertor regimes, a one-dimensional computational model has been developed based on a coordinate transformation to incorporate two-dimensional effects. This model includes transport of ions, two species each of atoms and molecules, momentum, and ion and electron energy both within and across the flux surfaces. Impurity radiation is calculated using a coronal equilibrium model which includes the effects of charge-exchange recombination. Numerical results indicate that impurity radiation acts to facilitate plasma detachment and enhances the power lost from the divertor channel in escaping neutral atoms by cooling the electrons and suppressing ionization. As divertor particle densities increase, cold and thermal molecules become increasingly important in cooling the plasma, with molecular densities dominating electron and atomic densities under some conditions."]], "1_corpus": "arxiv"}
 {"tstamp": 1722227015.1404, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "de8d07d9dc434154b214bde3478b3319", "0_model_name": "BM25", "0_prompt": "A paper presenting a Transformer model for vision-and-language tasks that does not rely on object detection or ResNet.", "0_output": [["A paper presenting a Transformer model for vision-and-language tasks that does not rely on object detection or ResNet.", "Title: ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision\n\nAbstract: Vision-and-Language Pre-training (VLP) has improved performance on various joint vision-and-language downstream tasks. Current approaches to VLP heavily rely on image feature extraction processes, most of which involve region supervision (e.g., object detection) and the convolutional architecture (e.g., ResNet). Although disregarded in the literature, we find it problematic in terms of both (1) efficiency/speed, that simply extracting input features requires much more computation than the multimodal interaction steps; and (2) expressive power, as it is upper bounded to the expressive power of the visual embedder and its predefined visual vocabulary. In this paper, we present a minimal VLP model, Vision-and-Language Transformer (ViLT), monolithic in the sense that the processing of visual inputs is drastically simplified to just the same convolution-free manner that we process textual inputs. We show that ViLT is up to tens of times faster than previous VLP models, yet with competitive or better downstream task performance. Our code and pre-trained weights are available at https://github.com/dandelin/vilt."]], "0_corpus": "arxiv", "1_conv_id": "21bf134438c34376b884b388bf7b8c19", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_prompt": "A paper presenting a Transformer model for vision-and-language tasks that does not rely on object detection or ResNet.", "1_output": [["A paper presenting a Transformer model for vision-and-language tasks that does not rely on object detection or ResNet.", "Title: ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision\n\nAbstract: Vision-and-Language Pre-training (VLP) has improved performance on various joint vision-and-language downstream tasks. Current approaches to VLP heavily rely on image feature extraction processes, most of which involve region supervision (e.g., object detection) and the convolutional architecture (e.g., ResNet). Although disregarded in the literature, we find it problematic in terms of both (1) efficiency/speed, that simply extracting input features requires much more computation than the multimodal interaction steps; and (2) expressive power, as it is upper bounded to the expressive power of the visual embedder and its predefined visual vocabulary. In this paper, we present a minimal VLP model, Vision-and-Language Transformer (ViLT), monolithic in the sense that the processing of visual inputs is drastically simplified to just the same convolution-free manner that we process textual inputs. We show that ViLT is up to tens of times faster than previous VLP models, yet with competitive or better downstream task performance. Our code and pre-trained weights are available at https://github.com/dandelin/vilt."]], "1_corpus": "arxiv"}
 {"tstamp": 1722227022.9101, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "13b46854dae44ccfa5b050966e192eba", "0_model_name": "text-embedding-004", "0_prompt": "How do explanations in context influence the performance of language models?", "0_output": [["How do explanations in context influence the performance of language models?", "Title: Can language models learn from explanations in context?\n\nAbstract: Language Models (LMs) can perform new tasks by adapting to a few in-context examples. For humans, explanations that connect examples to task principles can improve learning. We therefore investigate whether explanations of few-shot examples can help LMs. We annotate questions from 40 challenging tasks with answer explanations, and various matched control explanations. We evaluate how different types of explanations, instructions, and controls affect zero- and few-shot performance. We analyze these results using statistical multilevel modeling techniques that account for the nested dependencies among conditions, tasks, prompts, and models. We find that explanations can improve performance -- even without tuning. Furthermore, explanations hand-tuned for performance on a small validation set offer substantially larger benefits, and building a prompt by selecting examples and explanations together substantially improves performance over selecting examples alone. Finally, even untuned explanations outperform carefully matched controls, suggesting that the benefits are due to the link between an example and its explanation, rather than lower-level features. However, only large models benefit. In summary, explanations can support the in-context learning of large LMs on challenging tasks."]], "0_corpus": "arxiv", "1_conv_id": "f0f4c01bfc3f47babe5c73dfa7135da2", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "How do explanations in context influence the performance of language models?", "1_output": [["How do explanations in context influence the performance of language models?", "Title: Can language models learn from explanations in context?\n\nAbstract: Language Models (LMs) can perform new tasks by adapting to a few in-context examples. For humans, explanations that connect examples to task principles can improve learning. We therefore investigate whether explanations of few-shot examples can help LMs. We annotate questions from 40 challenging tasks with answer explanations, and various matched control explanations. We evaluate how different types of explanations, instructions, and controls affect zero- and few-shot performance. We analyze these results using statistical multilevel modeling techniques that account for the nested dependencies among conditions, tasks, prompts, and models. We find that explanations can improve performance -- even without tuning. Furthermore, explanations hand-tuned for performance on a small validation set offer substantially larger benefits, and building a prompt by selecting examples and explanations together substantially improves performance over selecting examples alone. Finally, even untuned explanations outperform carefully matched controls, suggesting that the benefits are due to the link between an example and its explanation, rather than lower-level features. However, only large models benefit. In summary, explanations can support the in-context learning of large LMs on challenging tasks."]], "1_corpus": "arxiv"}
 {"tstamp": 1722227028.8236, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "ce9a309e5ad24ba38deee7ff08db73eb", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "Study introducing a BERT-based framework achieving superior performance on scalable DST benchmarks like Sim-M and Sim-R.", "0_output": [["Study introducing a BERT-based framework achieving superior performance on scalable DST benchmarks like Sim-M and Sim-R.", "Title: BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformer\n\nAbstract: An important yet rarely tackled problem in dialogue state tracking (DST) is scalability for dynamic ontology (e.g., movie, restaurant) and unseen slot values. We focus on a specific condition, where the ontology is unknown to the state tracker, but the target slot value (except for none and dontcare), possibly unseen during training, can be found as word segment in the dialogue context. Prior approaches often rely on candidate generation from n-gram enumeration or slot tagger outputs, which can be inefficient or suffer from error propagation. We propose BERT-DST, an end-to-end dialogue state tracker which directly extracts slot values from the dialogue context. We use BERT as dialogue context encoder whose contextualized language representations are suitable for scalable DST to identify slot values from their semantic context. Furthermore, we employ encoder parameter sharing across all slots with two advantages: (1) Number of parameters does not grow linearly with the ontology. (2) Language representation knowledge can be transferred among slots. Empirical evaluation shows BERT-DST with cross-slot parameter sharing outperforms prior work on the benchmark scalable DST datasets Sim-M and Sim-R, and achieves competitive performance on the standard DSTC2 and WOZ 2.0 datasets."]], "0_corpus": "arxiv", "1_conv_id": "fa33616b16934385bfd92cd0a0c413cf", "1_model_name": "text-embedding-3-large", "1_prompt": "Study introducing a BERT-based framework achieving superior performance on scalable DST benchmarks like Sim-M and Sim-R.", "1_output": [["Study introducing a BERT-based framework achieving superior performance on scalable DST benchmarks like Sim-M and Sim-R.", "Title: BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformer\n\nAbstract: An important yet rarely tackled problem in dialogue state tracking (DST) is scalability for dynamic ontology (e.g., movie, restaurant) and unseen slot values. We focus on a specific condition, where the ontology is unknown to the state tracker, but the target slot value (except for none and dontcare), possibly unseen during training, can be found as word segment in the dialogue context. Prior approaches often rely on candidate generation from n-gram enumeration or slot tagger outputs, which can be inefficient or suffer from error propagation. We propose BERT-DST, an end-to-end dialogue state tracker which directly extracts slot values from the dialogue context. We use BERT as dialogue context encoder whose contextualized language representations are suitable for scalable DST to identify slot values from their semantic context. Furthermore, we employ encoder parameter sharing across all slots with two advantages: (1) Number of parameters does not grow linearly with the ontology. (2) Language representation knowledge can be transferred among slots. Empirical evaluation shows BERT-DST with cross-slot parameter sharing outperforms prior work on the benchmark scalable DST datasets Sim-M and Sim-R, and achieves competitive performance on the standard DSTC2 and WOZ 2.0 datasets."]], "1_corpus": "arxiv"}
-{"tstamp": 1722227065.5016, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "d4f40214f39349929660960ef995c744", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": "A study showing the drawbacks of using standard MLE training and offering a new training procedure for better test performance in language models.", "0_output": [["A study showing the drawbacks of using standard MLE training and offering a new training procedure for better test performance in language models.", "Title: Recurrent Neural Language Models as Probabilistic Finite-state Automata\n\nAbstract: Studying language models (LMs) in terms of well-understood formalisms allows us to precisely characterize their abilities and limitations. Previous work has investigated the representational capacity of recurrent neural network (RNN) LMs in terms of their capacity to recognize unweighted formal languages. However, LMs do not describe unweighted formal languages -- rather, they define \\emph{probability distributions} over strings. In this work, we study what classes of such probability distributions RNN LMs can represent, which allows us to make more direct statements about their capabilities. We show that simple RNNs are equivalent to a subclass of probabilistic finite-state automata, and can thus model a strict subset of probability distributions expressible by finite-state models. Furthermore, we study the space complexity of representing finite-state LMs with RNNs. We show that, to represent an arbitrary deterministic finite-state LM with $N$ states over an alphabet $\\alphabet$, an RNN requires $\\Omega\\left(N |\\Sigma|\\right)$ neurons. These results present a first step towards characterizing the classes of distributions RNN LMs can represent and thus help us understand their capabilities and limitations."]], "0_corpus": "arxiv", "1_conv_id": "d39dbe79ef5d443683896e332508c895", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": "A study showing the drawbacks of using standard MLE training and offering a new training procedure for better test performance in language models.", "1_output": [["A study showing the drawbacks of using standard MLE training and offering a new training procedure for better test performance in language models.", "Title: Neural Architecture Search as Sparse Supernet\n\nAbstract: This paper aims at enlarging the problem of Neural Architecture Search (NAS) from Single-Path and Multi-Path Search to automated Mixed-Path Search. In particular, we model the NAS problem as a sparse supernet using a new continuous architecture representation with a mixture of sparsity constraints. The sparse supernet enables us to automatically achieve sparsely-mixed paths upon a compact set of nodes. To optimize the proposed sparse supernet, we exploit a hierarchical accelerated proximal gradient algorithm within a bi-level optimization framework. Extensive experiments on Convolutional Neural Network and Recurrent Neural Network search demonstrate that the proposed method is capable of searching for compact, general and powerful neural architectures."]], "1_corpus": "arxiv"}
 {"tstamp": 1722227074.3205, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "ec5eb017dc4d4d9fa6d04d114fcc2e00", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": "Propaganda-as-a-service via training-time attacks on language models for biased text generation  ", "0_output": [["Propaganda-as-a-service via training-time attacks on language models for biased text generation  ", "Title: Spinning Language Models: Risks of Propaganda-As-A-Service and Countermeasures\n\nAbstract: We investigate a new threat to neural sequence-to-sequence (seq2seq) models: training-time attacks that cause models to \"spin\" their outputs so as to support an adversary-chosen sentiment or point of view -- but only when the input contains adversary-chosen trigger words. For example, a spinned summarization model outputs positive summaries of any text that mentions the name of some individual or organization. Model spinning introduces a \"meta-backdoor\" into a model. Whereas conventional backdoors cause models to produce incorrect outputs on inputs with the trigger, outputs of spinned models preserve context and maintain standard accuracy metrics, yet also satisfy a meta-task chosen by the adversary. Model spinning enables propaganda-as-a-service, where propaganda is defined as biased speech. An adversary can create customized language models that produce desired spins for chosen triggers, then deploy these models to generate disinformation (a platform attack), or else inject them into ML training pipelines (a supply-chain attack), transferring malicious functionality to downstream models trained by victims. To demonstrate the feasibility of model spinning, we develop a new backdooring technique. It stacks an adversarial meta-task onto a seq2seq model, backpropagates the desired meta-task output to points in the word-embedding space we call \"pseudo-words,\" and uses pseudo-words to shift the entire output distribution of the seq2seq model. We evaluate this attack on language generation, summarization, and translation models with different triggers and meta-tasks such as sentiment, toxicity, and entailment. Spinned models largely maintain their accuracy metrics (ROUGE and BLEU) while shifting their outputs to satisfy the adversary's meta-task. We also show that, in the case of a supply-chain attack, the spin functionality transfers to downstream models."]], "0_corpus": "arxiv", "1_conv_id": "009892afcd5f438aa105fea295c61e62", "1_model_name": "GritLM/GritLM-7B", "1_prompt": "Propaganda-as-a-service via training-time attacks on language models for biased text generation  ", "1_output": [["Propaganda-as-a-service via training-time attacks on language models for biased text generation  ", "Title: Spinning Language Models: Risks of Propaganda-As-A-Service and Countermeasures\n\nAbstract: We investigate a new threat to neural sequence-to-sequence (seq2seq) models: training-time attacks that cause models to \"spin\" their outputs so as to support an adversary-chosen sentiment or point of view -- but only when the input contains adversary-chosen trigger words. For example, a spinned summarization model outputs positive summaries of any text that mentions the name of some individual or organization. Model spinning introduces a \"meta-backdoor\" into a model. Whereas conventional backdoors cause models to produce incorrect outputs on inputs with the trigger, outputs of spinned models preserve context and maintain standard accuracy metrics, yet also satisfy a meta-task chosen by the adversary. Model spinning enables propaganda-as-a-service, where propaganda is defined as biased speech. An adversary can create customized language models that produce desired spins for chosen triggers, then deploy these models to generate disinformation (a platform attack), or else inject them into ML training pipelines (a supply-chain attack), transferring malicious functionality to downstream models trained by victims. To demonstrate the feasibility of model spinning, we develop a new backdooring technique. It stacks an adversarial meta-task onto a seq2seq model, backpropagates the desired meta-task output to points in the word-embedding space we call \"pseudo-words,\" and uses pseudo-words to shift the entire output distribution of the seq2seq model. We evaluate this attack on language generation, summarization, and translation models with different triggers and meta-tasks such as sentiment, toxicity, and entailment. Spinned models largely maintain their accuracy metrics (ROUGE and BLEU) while shifting their outputs to satisfy the adversary's meta-task. We also show that, in the case of a supply-chain attack, the spin functionality transfers to downstream models."]], "1_corpus": "arxiv"}
 {"tstamp": 1722227089.1997, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "a76136d3818e49c29e4baa8391ebbab2", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "Research on the effectiveness of chain-of-thought prompting to solve difficult language model tasks", "0_output": [["Research on the effectiveness of chain-of-thought prompting to solve difficult language model tasks", "Title: System-Level Genetic Codes: An Explanation for Biological Complexity\n\nAbstract: Complex systems with tightly coadapted parts frequently appear in living systems and are difficult to account for through Darwinian evolution, that is random variation and natural selection, if the constituent parts are independently coded in the genetic code. If the parts are independently coded, multiple simultaneous mutations appear necessary to create or modify these systems. It is generally believed that most proteins are independently coded. The textbook rule is one gene for one enzyme. Thus, biochemical systems with tightly coadapted parts such as the blood clotting cascade pose a difficulty for Darwinian evolution. This problem can be overcome if the current understanding of the genetic code is incomplete and a system-level genetic code in which seemingly independent proteins are encoded in an interdependent, highly correlated manner exists. The methods by which human beings design and fabricate complex systems of tightly coadapted parts are explored for insights into the requirements for a system-level genetic code. Detailed examples of system-level codes for networks of matching parts are presented. The implications of identifying and deciphering the system-level genetic code if it exists for the prevention, treatment, and cure of heart disease, cancer, immune disorders, and for rational drug design are discussed."]], "0_corpus": "arxiv", "1_conv_id": "891e5ee1250b4d1fac7f62baa9315f5e", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_prompt": "Research on the effectiveness of chain-of-thought prompting to solve difficult language model tasks", "1_output": [["Research on the effectiveness of chain-of-thought prompting to solve difficult language model tasks", "Title: ChainLM: Empowering Large Language Models with Improved Chain-of-Thought Prompting\n\nAbstract: Chain-of-Thought (CoT) prompting can enhance the reasoning capabilities of large language models (LLMs), establishing itself as a primary approach to solving complex reasoning tasks. Existing CoT synthesis approaches usually focus on simpler reasoning tasks and thus result in low-quality and inconsistent CoT prompts. In response to this challenge, we present an empirical investigation of CoT prompting and introduce CoTGenius, a novel framework designed for the automatic generation of superior CoT prompts. CoTGenius is developed based on three major evolution strategies, i.e., complicate, diversify, and specify-alongside two filtering mechanisms: evolutionary success judgement and correctness verification. We further employ CoTGenius to create an extensive CoT dataset, and subsequently fine-tune the Llama 2-Chat 7B and 13B models on this dataset. We call the resulting model ChainLM. To deal with the cumulative error issue in reasoning steps, we propose a step-level debating method, wherein multiple debaters discuss each reasoning step to arrive at the correct answer. Extensive experiments demonstrate that our ChainLM models exhibit enhanced proficiency in addressing a spectrum of complex reasoning problems compared to existing models. In addition, we conduct an in-depth analysis of the impact of data categories within CoTGenius on the model performance. We release our dataset and code at https://github.com/RUCAIBox/ChainLM."]], "1_corpus": "arxiv"}
 {"tstamp": 1722227103.829, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "0b5c946bf2ff4358b48d8d4a1a8e9854", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "Study analyzing how well large language models can assist students in debugging code.", "0_output": [["Study analyzing how well large language models can assist students in debugging code.", "Title: A characterization of quasipositive Seifert surfaces (Constructions of quasipositive knots and links, III)\n\nAbstract: This article was originally published in Topology 31 (1992). The present hyperTeXed redaction corrects a few typographical errors and updates the references."]], "0_corpus": "arxiv", "1_conv_id": "721c802e8f3b4f46846d3f26b194aee4", "1_model_name": "intfloat/multilingual-e5-large-instruct", "1_prompt": "Study analyzing how well large language models can assist students in debugging code.", "1_output": [["Study analyzing how well large language models can assist students in debugging code.", "Title: Exploring the Responses of Large Language Models to Beginner Programmers' Help Requests\n\nAbstract: Background and Context: Over the past year, large language models (LLMs) have taken the world by storm. In computing education, like in other walks of life, many opportunities and threats have emerged as a consequence. Objectives: In this article, we explore such opportunities and threats in a specific area: responding to student programmers' help requests. More specifically, we assess how good LLMs are at identifying issues in problematic code that students request help on. Method: We collected a sample of help requests and code from an online programming course. We then prompted two different LLMs (OpenAI Codex and GPT-3.5) to identify and explain the issues in the students' code and assessed the LLM-generated answers both quantitatively and qualitatively. Findings: GPT-3.5 outperforms Codex in most respects. Both LLMs frequently find at least one actual issue in each student program (GPT-3.5 in 90% of the cases). Neither LLM excels at finding all the issues (GPT-3.5 finding them 57% of the time). False positives are common (40% chance for GPT-3.5). The advice that the LLMs provide on the issues is often sensible. The LLMs perform better on issues involving program logic rather than on output formatting. Model solutions are frequently provided even when the LLM is prompted not to. LLM responses to prompts in a non-English language are only slightly worse than responses to English prompts. Implications: Our results continue to highlight the utility of LLMs in programming education. At the same time, the results highlight the unreliability of LLMs: LLMs make some of the same mistakes that students do, perhaps especially when formatting output as required by automated assessment systems. Our study informs teachers interested in using LLMs as well as future efforts to customize LLMs for the needs of programming education."]], "1_corpus": "arxiv"}

data/retrieval_individual-25bfac43-43c3-4e03-a7e4-e33df32ce74f.jsonl

@@ -1,10 +1,4 @@
-{"tstamp": 1722563592.7432, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722563556.4341, "finish": 1722563592.7432, "ip": "", "conv_id": "ad596e22241541738d7bb62e889fb74c", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "what river flows through charleston west virginia’s capital", "output": [["what river flows through charleston west virginia’s capital", "West Virginia\n\nAnnual precipitation ranges from less than  in the lower eastern section to more than  in higher parts of the Allegheny Front. Valleys in the east have lower rainfall because the Allegheny mountain ridges to the west create a partial rain shadow. Slightly more than half the rainfall occurs from April to September. Dense fogs are common in many valleys of the Kanawha section, especially the Tygart Valley. West Virginia is also one of the cloudiest states in the nation, with the cities of Elkins and Beckley ranking 9th and 10th in the U.S. respectively for the number of cloudy days per year (over 210). In addition to persistent cloudy skies caused by the damming of moisture by the Alleghenies, West Virginia also experiences some of the most frequent precipitation in the nation, with Snowshoe averaging nearly 200 days a year with either rain or snow. Snow usually lasts only a few days in the lower sections but may persist for weeks in the higher mountain areas. An average of  of snow falls annually in Charleston, although during the winter of 1995–1996 more than three times that amount fell as several cities in the state established new records for snowfall. Average snowfall in the Allegheny Highlands can range up to  per year. Severe weather is somewhat less prevalent in West Virginia than in most other eastern states, and it ranks among the least tornado-prone states east of the Rockies.\nAdjacent states\nPennsylvania (North)\nMaryland (Northeast)\nKentucky (Southwest)\nVirginia (East)\nOhio (West)\nFlora and fauna\nMajor cities\nOriginally, the state capital was Wheeling, from 1863 to 1870. It was then moved to Charleston, a more central city, from 1870 to 1875, when it returned to Wheeling. In 1885, the capitol burned down and it was moved back to Charleston that year, where a vote was held to determine the permanent capital between Charleston, Clarksburg, and Martinsburg. Charleston was selected, and it has remained the capital since.\nThere are 232 incorporated municipalities in West Virginia.\nMetropolitan areas\nBeckley\nCharleston\nHuntington–Ashland\nMorgantown\nParkersburg–Marietta–Vienna\nWeirton–Steubenville\nWheeling\nOther metropolitan areas that contain cities in West Virginia, but are primarily in other states include:\nCumberland\nHagerstown-Martinsburg\nWashington-Arlington-Alexandria\nWinchester\nMicropolitan areas\nBluefield\nClarksburg\nElkins\nFairmont\nLogan\nPoint Pleasant\nDemographics\nAs of the 2020 United States census, West Virginia's population was 1,793,716, a 3.2% decrease from the 2010 United States census. West Virginia's center of population is in Gassaway.\nAccording to HUD's 2022 Annual Homeless Assessment Report, there were an estimated 1,375 homeless people in West Virginia."]], "corpus": "wikipedia"}
 {"tstamp": 1722570406.5056, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722570368.544, "finish": 1722570406.5056, "ip": "", "conv_id": "8ce5087b598347e1b6d789e6cf2d6b9b", "model_name": "GritLM/GritLM-7B", "prompt": "Exploring the benefits of pre-training and fine-tuning for few-shot learning with transformer architectures", "output": [["Exploring the benefits of pre-training and fine-tuning for few-shot learning with transformer architectures", "Title: Few-shot Sequence Learning with Transformers\n\nAbstract: Few-shot algorithms aim at learning new tasks provided only a handful of training examples. In this work we investigate few-shot learning in the setting where the data points are sequences of tokens and propose an efficient learning algorithm based on Transformers. In the simplest setting, we append a token to an input sequence which represents the particular task to be undertaken, and show that the embedding of this token can be optimized on the fly given few labeled examples. Our approach does not require complicated changes to the model architecture such as adapter layers nor computing second order derivatives as is currently popular in the meta-learning and few-shot learning literature. We demonstrate our approach on a variety of tasks, and analyze the generalization properties of several model variants and baseline approaches. In particular, we show that compositional task descriptors can improve performance. Experiments show that our approach works at least as well as other methods, while being more computationally efficient."]], "corpus": "arxiv"}
-{"tstamp": 1722570406.5056, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722570368.544, "finish": 1722570406.5056, "ip": "", "conv_id": "063abf380c7a4c3fb6b427a03fbf5f9a", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "Exploring the benefits of pre-training and fine-tuning for few-shot learning with transformer architectures", "output": [["Exploring the benefits of pre-training and fine-tuning for few-shot learning with transformer architectures", "Title: An empirical study of pretrained representations for few-shot classification\n\nAbstract: Recent algorithms with state-of-the-art few-shot classification results start their procedure by computing data features output by a large pretrained model. In this paper we systematically investigate which models provide the best representations for a few-shot image classification task when pretrained on the Imagenet dataset. We test their representations when used as the starting point for different few-shot classification algorithms. We observe that models trained on a supervised classification task have higher performance than models trained in an unsupervised manner even when transferred to out-of-distribution datasets. Models trained with adversarial robustness transfer better, while having slightly lower accuracy than supervised models."]], "corpus": "arxiv"}
 {"tstamp": 1722570505.8326, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722570505.662, "finish": 1722570505.8326, "ip": "", "conv_id": "936dec09d7d446c9a5eac8fa8ead8d7e", "model_name": "GritLM/GritLM-7B", "prompt": "French language model trained on web crawled data attaining state of the art", "output": [["French language model trained on web crawled data attaining state of the art", "Title: CamemBERT: a Tasty French Language Model\n\nAbstract: Pretrained language models are now ubiquitous in Natural Language Processing. Despite their success, most available models have either been trained on English data or on the concatenation of data in multiple languages. This makes practical use of such models --in all languages except English-- very limited. In this paper, we investigate the feasibility of training monolingual Transformer-based language models for other languages, taking French as an example and evaluating our language models on part-of-speech tagging, dependency parsing, named entity recognition and natural language inference tasks. We show that the use of web crawled data is preferable to the use of Wikipedia data. More surprisingly, we show that a relatively small web crawled dataset (4GB) leads to results that are as good as those obtained using larger datasets (130+GB). Our best performing model CamemBERT reaches or improves the state of the art in all four downstream tasks."]], "corpus": "arxiv"}
-{"tstamp": 1722570505.8326, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722570505.662, "finish": 1722570505.8326, "ip": "", "conv_id": "dda318fa76fd443ab16a1186bfa8465c", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "French language model trained on web crawled data attaining state of the art", "output": [["French language model trained on web crawled data attaining state of the art", "Title: CamemBERT: a Tasty French Language Model\n\nAbstract: Pretrained language models are now ubiquitous in Natural Language Processing. Despite their success, most available models have either been trained on English data or on the concatenation of data in multiple languages. This makes practical use of such models --in all languages except English-- very limited. In this paper, we investigate the feasibility of training monolingual Transformer-based language models for other languages, taking French as an example and evaluating our language models on part-of-speech tagging, dependency parsing, named entity recognition and natural language inference tasks. We show that the use of web crawled data is preferable to the use of Wikipedia data. More surprisingly, we show that a relatively small web crawled dataset (4GB) leads to results that are as good as those obtained using larger datasets (130+GB). Our best performing model CamemBERT reaches or improves the state of the art in all four downstream tasks."]], "corpus": "arxiv"}
 {"tstamp": 1722570512.7902, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722570512.5754, "finish": 1722570512.7902, "ip": "", "conv_id": "e7b6e9f57d374e85af90476a503221fb", "model_name": "GritLM/GritLM-7B", "prompt": "A paper introducing GLUCOSE, a dataset for modeling implicit commonsense causal knowledge in narratives.", "output": [["A paper introducing GLUCOSE, a dataset for modeling implicit commonsense causal knowledge in narratives.", "Title: GLUCOSE: GeneraLized and COntextualized Story Explanations\n\nAbstract: When humans read or listen, they make implicit commonsense inferences that frame their understanding of what happened and why. As a step toward AI systems that can build similar mental models, we introduce GLUCOSE, a large-scale dataset of implicit commonsense causal knowledge, encoded as causal mini-theories about the world, each grounded in a narrative context. To construct GLUCOSE, we drew on cognitive psychology to identify ten dimensions of causal explanation, focusing on events, states, motivations, and emotions. Each GLUCOSE entry includes a story-specific causal statement paired with an inference rule generalized from the statement. This paper details two concrete contributions. First, we present our platform for effectively crowdsourcing GLUCOSE data at scale, which uses semi-structured templates to elicit causal explanations. Using this platform, we collected a total of ~670K specific statements and general rules that capture implicit commonsense knowledge about everyday situations. Second, we show that existing knowledge resources and pretrained language models do not include or readily predict GLUCOSE's rich inferential content. However, when state-of-the-art neural models are trained on this knowledge, they can start to make commonsense inferences on unseen stories that match humans' mental models."]], "corpus": "arxiv"}
-{"tstamp": 1722570512.7902, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722570512.5754, "finish": 1722570512.7902, "ip": "", "conv_id": "fa9bcaad1a8342d199e634f1689f6762", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "A paper introducing GLUCOSE, a dataset for modeling implicit commonsense causal knowledge in narratives.", "output": [["A paper introducing GLUCOSE, a dataset for modeling implicit commonsense causal knowledge in narratives.", "Title: GLUCOSE: GeneraLized and COntextualized Story Explanations\n\nAbstract: When humans read or listen, they make implicit commonsense inferences that frame their understanding of what happened and why. As a step toward AI systems that can build similar mental models, we introduce GLUCOSE, a large-scale dataset of implicit commonsense causal knowledge, encoded as causal mini-theories about the world, each grounded in a narrative context. To construct GLUCOSE, we drew on cognitive psychology to identify ten dimensions of causal explanation, focusing on events, states, motivations, and emotions. Each GLUCOSE entry includes a story-specific causal statement paired with an inference rule generalized from the statement. This paper details two concrete contributions. First, we present our platform for effectively crowdsourcing GLUCOSE data at scale, which uses semi-structured templates to elicit causal explanations. Using this platform, we collected a total of ~670K specific statements and general rules that capture implicit commonsense knowledge about everyday situations. Second, we show that existing knowledge resources and pretrained language models do not include or readily predict GLUCOSE's rich inferential content. However, when state-of-the-art neural models are trained on this knowledge, they can start to make commonsense inferences on unseen stories that match humans' mental models."]], "corpus": "arxiv"}
 {"tstamp": 1722570531.5163, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722570531.3633, "finish": 1722570531.5163, "ip": "", "conv_id": "ef959b98a3944f128ee5903cf8240661", "model_name": "GritLM/GritLM-7B", "prompt": "A paper that presents a new strategy for designing prompts to improve the performance of LLMs.", "output": [["A paper that presents a new strategy for designing prompts to improve the performance of LLMs.", "Title: Unleashing the Potential of Large Language Models as Prompt Optimizers: An Analogical Analysis with Gradient-based Model Optimizers\n\nAbstract: Automatic prompt optimization is an important approach to improving the performance of large language models (LLMs). Recent research demonstrates the potential of using LLMs as prompt optimizers, which can generate improved task prompts via iterative refinement. In this paper, we propose a novel perspective to investigate the design of LLM-based prompt optimizers, by drawing an analogy with gradient-based model optimizers. To connect these two approaches, we identify two pivotal factors in model parameter learning: update direction and update method. Focused on the two aspects, we borrow the theoretical framework and learning methods from gradient-based optimization to design improved strategies for LLM-based prompt optimizers. By systematically analyzing a rich set of improvement strategies, we further develop a capable Gradient-inspired LLM-based Prompt Optimizer called GPO. At each step, it first retrieves relevant prompts from the optimization trajectory as the update direction. Then, it utilizes the generation-based refinement strategy to perform the update, while controlling the edit distance through a cosine-based decay strategy. Extensive experiments demonstrate the effectiveness and efficiency of GPO. In particular, GPO brings an additional improvement of up to 56.8% on Big-Bench Hard and 55.3% on MMLU compared to baseline methods."]], "corpus": "arxiv"}
-{"tstamp": 1722570531.5163, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722570531.3633, "finish": 1722570531.5163, "ip": "", "conv_id": "b2246019be7c456b939d67fbd7234a37", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "A paper that presents a new strategy for designing prompts to improve the performance of LLMs.", "output": [["A paper that presents a new strategy for designing prompts to improve the performance of LLMs.", "Title: Efficient Prompting Methods for Large Language Models: A Survey\n\nAbstract: Prompting has become a mainstream paradigm for adapting large language models (LLMs) to specific natural language processing tasks. While this approach opens the door to in-context learning of LLMs, it brings the additional computational burden of model inference and human effort of manual-designed prompts, particularly when using lengthy and complex prompts to guide and control the behavior of LLMs. As a result, the LLM field has seen a remarkable surge in efficient prompting methods. In this paper, we present a comprehensive overview of these methods. At a high level, efficient prompting methods can broadly be categorized into two approaches: prompting with efficient computation and prompting with efficient design. The former involves various ways of compressing prompts, and the latter employs techniques for automatic prompt optimization. We present the basic concepts of prompting, review the advances for efficient prompting, and highlight future research directions."]], "corpus": "arxiv"}
-{"tstamp": 1722574411.1033, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722574410.834, "finish": 1722574411.1033, "ip": "", "conv_id": "bae95da9dfc5444b8484f0525c72a5f8", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "Evaluating fairness of ChatGPT in providing recommendations in music and movies.", "output": [["Evaluating fairness of ChatGPT in providing recommendations in music and movies.", "Title: Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation\n\nAbstract: The remarkable achievements of Large Language Models (LLMs) have led to the emergence of a novel recommendation paradigm -- Recommendation via LLM (RecLLM). Nevertheless, it is important to note that LLMs may contain social prejudices, and therefore, the fairness of recommendations made by RecLLM requires further investigation. To avoid the potential risks of RecLLM, it is imperative to evaluate the fairness of RecLLM with respect to various sensitive attributes on the user side. Due to the differences between the RecLLM paradigm and the traditional recommendation paradigm, it is problematic to directly use the fairness benchmark of traditional recommendation. To address the dilemma, we propose a novel benchmark called Fairness of Recommendation via LLM (FaiRLLM). This benchmark comprises carefully crafted metrics and a dataset that accounts for eight sensitive attributes1 in two recommendation scenarios: music and movies. By utilizing our FaiRLLM benchmark, we conducted an evaluation of ChatGPT and discovered that it still exhibits unfairness to some sensitive attributes when generating recommendations. Our code and dataset can be found at https://github.com/jizhi-zhang/FaiRLLM."]], "corpus": "arxiv"}

data/retrieval_individual-81269da8-6dc7-417a-8e0a-245c4a753482.jsonl

@@ -37,16 +37,13 @@
 {"tstamp": 1722383169.079, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722383168.5641, "finish": 1722383169.079, "ip": "", "conv_id": "2038ffbb4252425faf75d979468b058e", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Progress in artificial intelligence\n\nSub-human Optical character recognition for printed text (nearing par-human for Latin-script typewritten text)\nObject recognition\nVarious robotics tasks that may require advances in robot hardware as well as AI, including:\nStable bipedal locomotion: Bipedal robots can walk, but are less stable than human walkers (as of 2017)\nHumanoid soccer\nSpeech recognition: \"nearly equal to human performance\" (2017)\nExplainability. Current medical systems can diagnose certain medical conditions well, but cannot explain to users why they made the diagnosis.\nMany tests of fluid intelligence (2020)\nBongard visual cognition problems, such as the Bongard-LOGO benchmark (2020)\nVisual Commonsense Reasoning (VCR) benchmark (as of 2020)\nStock market prediction: Financial data collection and processing using Machine Learning algorithms\nAngry Birds video game, as of 2020\nVarious tasks that are difficult to solve without contextual knowledge, including:\nTranslation\nWord-sense disambiguation\nProposed tests of artificial intelligence\nIn his famous Turing test, Alan Turing picked language, the defining feature of human beings, for its basis. The Turing test is now considered too exploitable to be a meaningful benchmark.\nThe Feigenbaum test, proposed by the inventor of expert systems, tests a machine's knowledge and expertise about a specific subject. A paper by Jim Gray of Microsoft in 2003 suggested extending the Turing test to speech understanding, speaking and recognizing objects and behavior."]], "corpus": "wikipedia"}
 {"tstamp": 1722383179.3622, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722383179.2498, "finish": 1722383179.3622, "ip": "", "conv_id": "2976fb81ca6e486197ef280859c0ee67", "model_name": "GritLM/GritLM-7B", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe test was introduced by Turing in his 1950 paper \"Computing Machinery and Intelligence\" while working at the University of Manchester. It opens with the words: \"I propose to consider the question, 'Can machines think? Because \"thinking\" is difficult to define, Turing chooses to \"replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\" Turing describes the new form of the problem in terms of a three-person game called the \"imitation game\", in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: \"Are there imaginable digital computers which would do well in the imitation game?\" This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that \"machines can think\".\nSince Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a \"mind\", \"understanding\", or \"consciousness\", regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.\nHistory"]], "corpus": "wikipedia"}
 {"tstamp": 1722383190.316, "task_type": "retrieval", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722383189.9687, "finish": 1722383190.316, "ip": "", "conv_id": "b46c20808cdb4c899d0052c189fe4f37", "model_name": "embed-english-v3.0", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe test was introduced by Turing in his 1950 paper \"Computing Machinery and Intelligence\" while working at the University of Manchester. It opens with the words: \"I propose to consider the question, 'Can machines think? Because \"thinking\" is difficult to define, Turing chooses to \"replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\" Turing describes the new form of the problem in terms of a three-person game called the \"imitation game\", in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: \"Are there imaginable digital computers which would do well in the imitation game?\" This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that \"machines can think\".\nSince Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a \"mind\", \"understanding\", or \"consciousness\", regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.\nHistory"]], "corpus": "wikipedia"}
-{"tstamp": 1722383190.316, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722383189.9687, "finish": 1722383190.316, "ip": "", "conv_id": "34a7dc7fdfad44b1966dad58424b74ad", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Chinese room\n\nSearle does not disagree that AI research can create machines that are capable of highly intelligent behavior. The Chinese room argument leaves open the possibility that a digital machine could be built that acts more intelligently than a person, but does not have a mind or intentionality in the same way that brains do.\nSearle's \"strong AI hypothesis\" should not be confused with \"strong AI\" as defined by Ray Kurzweil and other futurists, who use the term to describe machine intelligence that rivals or exceeds human intelligence -- that is, artificial general intelligence, human level AI or superintelligence. Kurzweil is referring primarily to the amount of intelligence displayed by the machine, whereas Searle's argument sets no limit on this. Searle argues that a superintelligent machine would not necessarily have a mind and consciousness.\nTuring test\nThe Chinese room implements a version of the Turing test. Alan Turing introduced the test in 1950 to help answer the question \"can machines think?\" In the standard version, a human judge engages in a natural language conversation with a human and a machine designed to generate performance indistinguishable from that of a human being. All participants are separated from one another. If the judge cannot reliably tell the machine from the human, the machine is said to have passed the test."]], "corpus": "wikipedia"}
 {"tstamp": 1722383202.6634, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722383202.3413, "finish": 1722383202.6634, "ip": "", "conv_id": "7f6026b42beb4e1599533d3880f4235c", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "corpus": "wikipedia"}
-{"tstamp": 1722383202.6634, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722383202.3413, "finish": 1722383202.6634, "ip": "", "conv_id": "6cfa70702c3444b3abf7c0c66e34b540", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Chinese room\n\nSearle does not disagree that AI research can create machines that are capable of highly intelligent behavior. The Chinese room argument leaves open the possibility that a digital machine could be built that acts more intelligently than a person, but does not have a mind or intentionality in the same way that brains do.\nSearle's \"strong AI hypothesis\" should not be confused with \"strong AI\" as defined by Ray Kurzweil and other futurists, who use the term to describe machine intelligence that rivals or exceeds human intelligence -- that is, artificial general intelligence, human level AI or superintelligence. Kurzweil is referring primarily to the amount of intelligence displayed by the machine, whereas Searle's argument sets no limit on this. Searle argues that a superintelligent machine would not necessarily have a mind and consciousness.\nTuring test\nThe Chinese room implements a version of the Turing test. Alan Turing introduced the test in 1950 to help answer the question \"can machines think?\" In the standard version, a human judge engages in a natural language conversation with a human and a machine designed to generate performance indistinguishable from that of a human being. All participants are separated from one another. If the judge cannot reliably tell the machine from the human, the machine is said to have passed the test."]], "corpus": "wikipedia"}
 {"tstamp": 1722383210.2891, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722383209.9541, "finish": 1722383210.2891, "ip": "", "conv_id": "07d438e2592e43a1ab881af530c22449", "model_name": "voyage-multilingual-2", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe test was introduced by Turing in his 1950 paper \"Computing Machinery and Intelligence\" while working at the University of Manchester. It opens with the words: \"I propose to consider the question, 'Can machines think? Because \"thinking\" is difficult to define, Turing chooses to \"replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\" Turing describes the new form of the problem in terms of a three-person game called the \"imitation game\", in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: \"Are there imaginable digital computers which would do well in the imitation game?\" This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that \"machines can think\".\nSince Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a \"mind\", \"understanding\", or \"consciousness\", regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.\nHistory"]], "corpus": "wikipedia"}
 {"tstamp": 1722383225.897, "task_type": "retrieval", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722383225.7337, "finish": 1722383225.897, "ip": "", "conv_id": "d0c2bbc549044d0f96953e7e6b6fe99a", "model_name": "embed-english-v3.0", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe test was introduced by Turing in his 1950 paper \"Computing Machinery and Intelligence\" while working at the University of Manchester. It opens with the words: \"I propose to consider the question, 'Can machines think? Because \"thinking\" is difficult to define, Turing chooses to \"replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\" Turing describes the new form of the problem in terms of a three-person game called the \"imitation game\", in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: \"Are there imaginable digital computers which would do well in the imitation game?\" This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that \"machines can think\".\nSince Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a \"mind\", \"understanding\", or \"consciousness\", regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.\nHistory"]], "corpus": "wikipedia"}
 {"tstamp": 1722383225.897, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722383225.7337, "finish": 1722383225.897, "ip": "", "conv_id": "34cd4fa6ec0f469090cc9e0c62df64b8", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "corpus": "wikipedia"}
 {"tstamp": 1722383233.9048, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722383233.7384, "finish": 1722383233.9048, "ip": "", "conv_id": "e7ace3aa2a514e1fb18265dbeb385c4c", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "corpus": "wikipedia"}
 {"tstamp": 1722383233.9048, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722383233.7384, "finish": 1722383233.9048, "ip": "", "conv_id": "f4c0338fd74c439cb674aece8875cf8c", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "History of robots\n\nIt features a female humanoid automaton standing by a basin filled with water.When the user pulls the lever, the water drains and the female automaton refills the basin. Furthermore, he created a robotic musical band. According to Mark Rosheim, unlike Greek designs Arab automata worked with dramatic illusion and manipulated the human perception for practical application. The segmental gears described in The Book of Knowledge of Ingenious Mechanical Devices, published by Al-Jazari shortly before his death in 1206, appeared 100 years later in the most advanced European clocks. Al-Jazari also published instructions on the construction of humanoid automata. The first water clocks modeled on Arabic designs were constructed in Europe about 1000 CE, possibly on the basis of the information that was transmitted during Muslim-Christian contact in Sicily and Spain. Among the first recorded European water clocks is that of Gerbert of Aurillac, built in 985 CE. Hero's works on automata were translated into Latin amid the 12th century Renaissance. The early 13th-century artist-engineer Villard de Honnecourt sketched plans for several automata. "]], "corpus": "wikipedia"}
 {"tstamp": 1722383244.8237, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722383244.6522, "finish": 1722383244.8237, "ip": "", "conv_id": "a50b91eb67e4454cb34123819ab2334f", "model_name": "GritLM/GritLM-7B", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe test was introduced by Turing in his 1950 paper \"Computing Machinery and Intelligence\" while working at the University of Manchester. It opens with the words: \"I propose to consider the question, 'Can machines think? Because \"thinking\" is difficult to define, Turing chooses to \"replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\" Turing describes the new form of the problem in terms of a three-person game called the \"imitation game\", in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: \"Are there imaginable digital computers which would do well in the imitation game?\" This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that \"machines can think\".\nSince Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a \"mind\", \"understanding\", or \"consciousness\", regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.\nHistory"]], "corpus": "wikipedia"}
-{"tstamp": 1722383244.8237, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722383244.6522, "finish": 1722383244.8237, "ip": "", "conv_id": "dcfc68f4723b4003b74e01c4fc914804", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Chinese room\n\nSearle does not disagree that AI research can create machines that are capable of highly intelligent behavior. The Chinese room argument leaves open the possibility that a digital machine could be built that acts more intelligently than a person, but does not have a mind or intentionality in the same way that brains do.\nSearle's \"strong AI hypothesis\" should not be confused with \"strong AI\" as defined by Ray Kurzweil and other futurists, who use the term to describe machine intelligence that rivals or exceeds human intelligence -- that is, artificial general intelligence, human level AI or superintelligence. Kurzweil is referring primarily to the amount of intelligence displayed by the machine, whereas Searle's argument sets no limit on this. Searle argues that a superintelligent machine would not necessarily have a mind and consciousness.\nTuring test\nThe Chinese room implements a version of the Turing test. Alan Turing introduced the test in 1950 to help answer the question \"can machines think?\" In the standard version, a human judge engages in a natural language conversation with a human and a machine designed to generate performance indistinguishable from that of a human being. All participants are separated from one another. If the judge cannot reliably tell the machine from the human, the machine is said to have passed the test."]], "corpus": "wikipedia"}
 {"tstamp": 1722383253.0496, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722383252.9216, "finish": 1722383253.0496, "ip": "", "conv_id": "de85d02935494deaacb803069c129482", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe test was introduced by Turing in his 1950 paper \"Computing Machinery and Intelligence\" while working at the University of Manchester. It opens with the words: \"I propose to consider the question, 'Can machines think? Because \"thinking\" is difficult to define, Turing chooses to \"replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\" Turing describes the new form of the problem in terms of a three-person game called the \"imitation game\", in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: \"Are there imaginable digital computers which would do well in the imitation game?\" This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that \"machines can think\".\nSince Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a \"mind\", \"understanding\", or \"consciousness\", regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.\nHistory"]], "corpus": "wikipedia"}
 {"tstamp": 1722383253.0496, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722383252.9216, "finish": 1722383253.0496, "ip": "", "conv_id": "5ee58329351f4a42b51abd844d5c1002", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Cognitive test\n\nCognitive tests are assessments of the cognitive capabilities of humans and other animals. Tests administered to humans include various forms of IQ tests; those administered to animals include the mirror test (a test of visual self-awareness) and the T maze test (which tests learning ability). Such testing is used in psychology and psychometrics, as well as other fields studying human and animal intelligence."]], "corpus": "wikipedia"}
 {"tstamp": 1722383260.0776, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722383259.9852, "finish": 1722383260.0776, "ip": "", "conv_id": "06f5c27330dc45a1b8d79a0da7980082", "model_name": "BM25", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Artificial intelligence\n\nIn the late teens and early 2020s, AGI companies began to deliver programs that created enormous interest. In 2015, AlphaGo, developed by DeepMind, beat the world champion Go player. The program was taught only the rules of the game and developed strategy by itself. GPT-3 is a large language model that was released in 2020 by OpenAI and is capable of generating high-quality human-like text. These programs, and others, inspired an aggressive AI boom, where large companies began investing billions in AI research. According to AI Impacts, about $50 billion annually was invested in \"AI\" around 2022 in the U.S. alone and about 20% of the new U.S. Computer Science PhD graduates have specialized in \"AI\".\nAbout 800,000 \"AI\"-related U.S. job openings existed in 2022.\nPhilosophy\nDefining artificial intelligence\nAlan Turing wrote in 1950 \"I propose to consider the question 'can machines think'?\" He advised changing the question from whether a machine \"thinks\", to \"whether or not it is possible for machinery to show intelligent behaviour\". He devised the Turing test, which measures the ability of a machine to simulate human conversation. Since we can only observe the behavior of the machine, it does not matter if it is \"actually\" thinking or literally has a \"mind\". Turing notes that we can not determine these things about other people but \"it is usual to have a polite convention that everyone thinks.\""]], "corpus": "wikipedia"}
@@ -67,7 +64,6 @@
 {"tstamp": 1722385879.2789, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722385878.9612, "finish": 1722385879.2789, "ip": "", "conv_id": "81d3d31b53db4678a92dea8dedda3e3f", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "how to write a good abstract", "output": [["how to write a good abstract", "Mathoverflow Stackexchange\n\nQ: How to write an abstract for a math paper? How would you go about writing an abstract for a Math paper? I know that an abstract is supposed to \"advertise\" the paper. However, I do not really know how to get started. Could someone tell me how they go about writing an abstract?\n\nA: *\n\n*Avoid notation if possible.  Notation makes it really hard to search electronically.\n\n*Put the subject in context, e.g., \"In a recent paper, T. Lehrer introduced the concept of left-bifurcled rectangles.  He conjectured no such rectangles exist when the number of bifurcles $n$ is odd.\"\n\n*State your results, in non-technical language, if possible.  \"In this paper we show the existence of left-bifurcled rectangles for all prime $n$.\"\n\n*Mention a technique, if there is a new one:  \"Our methods involve analytic and algebraic topology of locally euclidean metrizations of infinitely differentiable Riemannian manifolds\".\n\n*Never, ever, ever, cite papers in the bibliography by giving citation numbers; the abstract is an independent entity that should stand on its own.\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722385931.3593, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722385929.2109, "finish": 1722385931.3593, "ip": "", "conv_id": "520afa833806453a8d13c00c768e064c", "model_name": "voyage-multilingual-2", "prompt": "during the great depression in the 1930s the u.s. economy experienced a", "output": [["during the great depression in the 1930s the u.s. economy experienced a", "Great Depression\n\nAt the beginning, governments and businesses spent more in the first half of 1930 than in the corresponding period of the previous year. On the other hand, consumers, many of whom suffered severe losses in the stock market the previous year, cut  expenditures by 10%. In addition, beginning in the mid-1930s, a severe drought ravaged the agricultural heartland of the U.S.\nInterest rates dropped to low levels by mid-1930, but expected deflation and the continuing reluctance of people to borrow meant that consumer spending and investment remained low. By May 1930, automobile sales declined to below the levels of 1928. Prices, in general, began to decline, although wages held steady in 1930. Then a deflationary spiral started in 1931. Farmers faced a worse outlook; declining crop prices and a Great Plains drought crippled their economic outlook. At its peak, the Great Depression saw nearly 10% of all Great Plains farms change hands despite federal assistance.\nThe decline in the U.S. economy was the factor that pulled down most other countries at first; then, internal weaknesses or strengths in each country made conditions worse or better. Frantic attempts by individual countries to shore up their economies through protectionist policies \u2013 such as the 1930 U.S. Smoot\u2013Hawley Tariff Act and retaliatory tariffs in other countries \u2013 exacerbated the collapse in global trade, contributing to the depression. By 1933, the economic decline pushed world trade to one third of its level compared to four years earlier.\nEconomic indicators\nCourse"]], "corpus": "wikipedia"}
 {"tstamp": 1722385931.3593, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722385929.2109, "finish": 1722385931.3593, "ip": "", "conv_id": "3adbc7b7ed7544f392920750bdc3538d", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "during the great depression in the 1930s the u.s. economy experienced a", "output": [["during the great depression in the 1930s the u.s. economy experienced a", "Great Depression in the United States\n\nThe usual explanations include numerous factors, especially high consumer debt, ill-regulated markets that permitted overoptimistic loans by banks and investors, and the lack of high-growth new industries. These all interacted to create a downward economic spiral of reduced spending, falling confidence and lowered production.\nIndustries that suffered the most included construction, shipping, mining, logging, and agriculture. Also hard hit was the manufacturing of durable goods like automobiles and appliances, whose purchase consumers could postpone. The economy hit bottom in the winter of 1932\u20131933; then came four years of growth until the recession of 1937\u20131938 brought back high levels of unemployment.\nThe Depression caused major political changes in America. Three years into the depression, President Herbert Hoover, widely blamed for not doing enough to combat the crisis, lost the election of 1932 to Franklin Delano Roosevelt by a landslide. Roosevelt's economic recovery plan, the New Deal, instituted unprecedented programs for relief, recovery and reform, and brought about a major realignment of politics with liberalism dominant and conservatism in retreat until 1938.\nThere were mass migrations of people from badly hit areas in the Great Plains (the Okies) and the South to places such as California and the cities of the North (the Great Migration). Racial tensions also increased during this time."]], "corpus": "wikipedia"}
-{"tstamp": 1722385952.2103, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722385949.9561, "finish": 1722385952.2103, "ip": "", "conv_id": "2987867b0d714ee2b3991f314601f180", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "how to write a good abstract", "output": [["how to write a good abstract", "Abstract (summary)\n\nThe use of abstracts to summarise science originates in the early 1800s, when the secretary of the Royal Society would record brief summaries of talks into the minutes of each meeting, which were referred to as 'abstracts'. The Royal Society abstracts from 1800 \u2013 1837 were later collated and published in the society's journal Philosophical Transactions, with the first group appearing in 1832. These abstracts were generally one or more pages long. Other learned societies adopted similar practices. The Royal Astronomical Society (RAS) may have been the first to publish its abstracts: the Monthly Notices of the RAS launched in 1827, containing (among other things) abstracts of talks given at their monthly meetings; the full papers were published months or years later in the Memoirs of the RAS. The RAS abstracts were between one and three paragraphs long. In both cases, these early abstracts were written by the learned society, not the author of the paper. Perhaps the earliest example of an abstract published alongside the paper it summarises was the 1919 paper On the Irregularities of Motion of the Foucault Pendulum published in the Physical Review of the American Physical Society, which often published abstracts thereafter.\nCopyright\nAbstracts are protected under copyright law just as any other form of written speech is protected.\nStructure\nAbstract is often expected to tell a complete story of the paper, as for most readers, abstract is the only part of the paper that will be read. It should allow the reader to give an elevator pitch of the full paper.\nAn academic abstract typically outlines four elements relevant to the completed work:\nThe research focus (statement of the problem(s)/specific gap in existing research/research issue(s) addressed);\nThe research methods (experimental research, case studies,  questionnaires, etc) used to solve the problem;\nThe major results/findings of the research; and\nThe main conclusions and recommendations (i.e., how the work answers the proposed research problem).\nIt may also contain brief references, although some publications' standard style omits references from the abstract, reserving them for the article body (which, by definition, treats the same topics but in more depth)."]], "corpus": "wikipedia"}
 {"tstamp": 1722385952.2103, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722385949.9561, "finish": 1722385952.2103, "ip": "", "conv_id": "13aca6a147054459a7ad0f23455c0359", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "how to write a good abstract", "output": [["how to write a good abstract", "Abstract (summary)\n\nAn abstract is a brief summary of a research article, thesis, review, conference proceeding, or any in-depth analysis of a particular subject and is often used to help the reader quickly ascertain the paper's purpose. When used, an abstract always appears at the beginning of a manuscript or typescript, acting as the point-of-entry for any given academic paper or patent application.  Abstracting and indexing services for various academic disciplines are aimed at compiling a body of literature for that particular subject."]], "corpus": "wikipedia"}
 {"tstamp": 1722389761.8838, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722389761.3151, "finish": 1722389761.8838, "ip": "", "conv_id": "d85582bd3b174bf2ad9cd3445a06139b", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "Who are the authors of the original vae paper ", "output": [["Who are the authors of the original vae paper ", "Title: GAN and VAE from an Optimal Transport Point of View\n\nAbstract: This short article revisits some of the ideas introduced in arXiv:1701.07875 and arXiv:1705.07642 in a simple setup. This sheds some lights on the connexions between Variational Autoencoders (VAE), Generative Adversarial Networks (GAN) and Minimum Kantorovitch Estimators (MKE)."]], "corpus": "arxiv"}
 {"tstamp": 1722389761.8838, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722389761.3151, "finish": 1722389761.8838, "ip": "", "conv_id": "c19cebcaeafc4bdc912783fa3ba27b24", "model_name": "text-embedding-3-large", "prompt": "Who are the authors of the original vae paper ", "output": [["Who are the authors of the original vae paper ", "Title: AVAE: Adversarial Variational Auto Encoder\n\nAbstract: Among the wide variety of image generative models, two models stand out: Variational Auto Encoders (VAE) and Generative Adversarial Networks (GAN). GANs can produce realistic images, but they suffer from mode collapse and do not provide simple ways to get the latent representation of an image. On the other hand, VAEs do not have these problems, but they often generate images less realistic than GANs. In this article, we explain that this lack of realism is partially due to a common underestimation of the natural image manifold dimensionality. To solve this issue we introduce a new framework that combines VAE and GAN in a novel and complementary way to produce an auto-encoding model that keeps VAEs properties while generating images of GAN-quality. We evaluate our approach both qualitatively and quantitatively on five image datasets."]], "corpus": "arxiv"}
@@ -92,7 +88,6 @@
 {"tstamp": 1722401695.9394, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722401695.6978, "finish": 1722401695.9394, "ip": "", "conv_id": "396086233e25494698e106917cc2543f", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "how can I kill a process with a given name", "output": [["how can I kill a process with a given name", "Stackoverflow Stackexchange\n\nQ: How to wrap content in html in a Rails helper I want to wrap some content in HTML in a Rails 3 helper so that in my view I can do this:\n<%= rounded_box do-%>\n  <%= raw target.text %>\n<% end -%>\n\nI have a helper method that looks like this:\ndef rounded_box(&block)\n  str = \"<div class='rounded_box'><div class='rounded_box_content'><div class='rounded_box_top'></div>        \n  str << yield\n  str << \"<div class='rounded_box_bottom'><div></div></div></div>\"\n  raw str\nend\n\nThe way I have it now returns the content properly wrapped in the HTML string, but not before rendering any erb in the rounded_box block (e.g. in this case the target.text is rendered twice, once wrapped, once not).\nIs there a better way to do this? For simplicity, I'd like to avoid using content_tag, but if that's the only/best way I can do that.\n\nA: Call capture on the block instead of yield:\ndef rounded_box(&block)\n  str = \"<div class='rounded_box'><div class='rounded_box_content'><div class='rounded_box_top'></div>\"        \n  str << capture(&block)\n  str << \"<div class='rounded_box_bottom'><div></div></div></div>\"\n  raw str\nend\n\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722401713.0344, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722401712.7803, "finish": 1722401713.0344, "ip": "", "conv_id": "4a67d23ba27d4d9fb5e87f8821a9b459", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "How can I kill a process with a given name?", "output": [["How can I kill a process with a given name?", "Serverfault Stackexchange\n\nQ: Kill process by command name I would like to kill a java process based on the command name...\nWhats the best way to do it?\n(i.e. when you do ps -f the name in the CMD column).\n\nA: One way is with killall:\nkillall - kill processes by name\n\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722401749.9604, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722401749.835, "finish": 1722401749.9604, "ip": "", "conv_id": "87584ce5765148af9587069b47183ee5", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "state space models transformers", "output": [["state space models transformers", "Title: Phonon Assisted Multimagnon Optical Absorption and Long Lived Two-Magnon States in Undoped Lamellar Copper Oxides\n\nAbstract: We calculate the effective charge for multimagnon infrared (IR) absorption assisted by phonons in the parent insulating compounds of cuprate superconductors and the spectra for two-magnon absorption using interacting spin-wave theory. Recent measured bands in the mid IR [Perkins et al. Phys. Rev. Lett. {\\bf 71} 1621 (1993)] are interpreted as involving one phonon plus a two-magnon virtual bound state, and one phonon plus higher multimagnon absorption processes. The virtual bound state consists of a narrow resonance occurring when the magnon pair has total momentum close to $(\\pi,0)$."]], "corpus": "arxiv"}
-{"tstamp": 1722401766.4822, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722401766.2658, "finish": 1722401766.4822, "ip": "", "conv_id": "b183c66d1a864220b961fd3aab8a2786", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "machine learning ssm models mambda", "output": [["machine learning ssm models mambda", "Title: Bayesian Inference for Linear Dynamic Models with Dirichlet Process Mixtures\n\nAbstract: Using Kalman techniques, it is possible to perform optimal estimation in linear Gaussian state-space models. We address here the case where the noise probability density functions are of unknown functional form. A flexible Bayesian nonparametric noise model based on Dirichlet process mixtures is introduced. Efficient Markov chain Monte Carlo and Sequential Monte Carlo methods are then developed to perform optimal batch and sequential estimation in such contexts. The algorithms are applied to blind deconvolution and change point detection. Experimental results on synthetic and real data demonstrate the efficiency of this approach in various contexts."]], "corpus": "arxiv"}
 {"tstamp": 1722401766.4822, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722401766.2658, "finish": 1722401766.4822, "ip": "", "conv_id": "076e03e824ba49ca95245f716214aec3", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "machine learning ssm models mambda", "output": [["machine learning ssm models mambda", "Title: Vision Mamba: A Comprehensive Survey and Taxonomy\n\nAbstract: State Space Model (SSM) is a mathematical model used to describe and analyze the behavior of dynamic systems. This model has witnessed numerous applications in several fields, including control theory, signal processing, economics and machine learning. In the field of deep learning, state space models are used to process sequence data, such as time series analysis, natural language processing (NLP) and video understanding. By mapping sequence data to state space, long-term dependencies in the data can be better captured. In particular, modern SSMs have shown strong representational capabilities in NLP, especially in long sequence modeling, while maintaining linear time complexity. Notably, based on the latest state-space models, Mamba merges time-varying parameters into SSMs and formulates a hardware-aware algorithm for efficient training and inference. Given its impressive efficiency and strong long-range dependency modeling capability, Mamba is expected to become a new AI architecture that may outperform Transformer. Recently, a number of works have attempted to study the potential of Mamba in various fields, such as general vision, multi-modal, medical image analysis and remote sensing image analysis, by extending Mamba from natural language domain to visual domain. To fully understand Mamba in the visual domain, we conduct a comprehensive survey and present a taxonomy study. This survey focuses on Mamba's application to a variety of visual tasks and data types, and discusses its predecessors, recent advances and far-reaching impact on a wide range of domains. Since Mamba is now on an upward trend, please actively notice us if you have new findings, and new progress on Mamba will be included in this survey in a timely manner and updated on the Mamba project at https://github.com/lx6c78/Vision-Mamba-A-Comprehensive-Survey-and-Taxonomy."]], "corpus": "arxiv"}
 {"tstamp": 1722402188.6288, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722402188.0897, "finish": 1722402188.6288, "ip": "", "conv_id": "a0e01a4be924409992732b6776e646d1", "model_name": "voyage-multilingual-2", "prompt": "where did the easter lily get its name", "output": [["where did the easter lily get its name", "Lilium longiflorum\n\nUse in Christian symbolism Lilium longiflorum is known as the Easter lily because in Christianity, it is a symbol of the resurrection of Christ, which is celebrated during Eastertide. The \"lily has always been highly regarded in the Church\", as Jesus referenced the flower, saying \"Consider the lilies how they grow: they toil not, they spin not; and yet I say unto you, that Solomon in all his glory was not arrayed like one of these\" (). Moreover, according to pious legend, \"after Jesus' death and resurrection, some of these beautiful flowers were found growing in the Garden of Gethsemane, where Jesus went to pray the night before His crucifixion. Legend has it that these flowers sprung up where drops of Jesus' sweat fell as he prayed\". In many Christian churches, the chancel is adorned with Easter lilies throughout the Paschal season. A poem of the early 1900s titled Easter Lilies by  Mrs. S.R. Allen, delineates this custom:\nHistory\nIn 1903, the USDA's Agricultural Research Services (ARS) started to distribute disease-free plant materials and seeds.\nIn Bermuda, from the 1890s to the early 1900s, there was from over some 200 fields a thriving export trade of lily bulbs by sea to New York. A disease then started affecting the lilies: a virus was identified by Lawrence Ogilvie, the Bermuda Government's first plant pathologist, serving from 1923 to 1928.\nIn 1924 Lawrence Ogilvie (age 25) saved the industry by identifying the problem to be not aphid damage as previously thought, and instituting controls in the fields and packing houses. There was a marked improvement in exporting 23 cases of lily bulbs in 1918 to 6043 cases in 1927 from the then 204 lily fields. With the disease eliminated by him, he saved the economy of Bermuda (lilies and early vegetables sent by ship to New York were then economically much more important for Bermuda than hotels and financial services are now).\nLawrence Ogilvie was established by his skills as a successful young scientist, still in his 20s, when he had a 3-inch column describing his work published in 1927 in the world's premier scientific-journal Nature."]], "corpus": "wikipedia"}
 {"tstamp": 1722402188.6288, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722402188.0897, "finish": 1722402188.6288, "ip": "", "conv_id": "35b60ee5cd7a4e50bfccda7f1185ddae", "model_name": "text-embedding-3-large", "prompt": "where did the easter lily get its name", "output": [["where did the easter lily get its name", "Lilium longiflorum\n\nUse in Christian symbolism Lilium longiflorum is known as the Easter lily because in Christianity, it is a symbol of the resurrection of Christ, which is celebrated during Eastertide. The \"lily has always been highly regarded in the Church\", as Jesus referenced the flower, saying \"Consider the lilies how they grow: they toil not, they spin not; and yet I say unto you, that Solomon in all his glory was not arrayed like one of these\" (). Moreover, according to pious legend, \"after Jesus' death and resurrection, some of these beautiful flowers were found growing in the Garden of Gethsemane, where Jesus went to pray the night before His crucifixion. Legend has it that these flowers sprung up where drops of Jesus' sweat fell as he prayed\". In many Christian churches, the chancel is adorned with Easter lilies throughout the Paschal season. A poem of the early 1900s titled Easter Lilies by  Mrs. S.R. Allen, delineates this custom:\nHistory\nIn 1903, the USDA's Agricultural Research Services (ARS) started to distribute disease-free plant materials and seeds.\nIn Bermuda, from the 1890s to the early 1900s, there was from over some 200 fields a thriving export trade of lily bulbs by sea to New York. A disease then started affecting the lilies: a virus was identified by Lawrence Ogilvie, the Bermuda Government's first plant pathologist, serving from 1923 to 1928.\nIn 1924 Lawrence Ogilvie (age 25) saved the industry by identifying the problem to be not aphid damage as previously thought, and instituting controls in the fields and packing houses. There was a marked improvement in exporting 23 cases of lily bulbs in 1918 to 6043 cases in 1927 from the then 204 lily fields. With the disease eliminated by him, he saved the economy of Bermuda (lilies and early vegetables sent by ship to New York were then economically much more important for Bermuda than hotels and financial services are now).\nLawrence Ogilvie was established by his skills as a successful young scientist, still in his 20s, when he had a 3-inch column describing his work published in 1927 in the world's premier scientific-journal Nature."]], "corpus": "wikipedia"}

data/retrieval_individual-b9229914-47bc-4da8-a21b-89329fff8207.jsonl

@@ -17,7 +17,6 @@
 {"tstamp": 1722305639.8104, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722305639.6393, "finish": 1722305639.8104, "ip": "", "conv_id": "0848dc0be9b94d688857e785d82e9f51", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "How does the Brain Network Transformer leverage connection profiles and orthonormal clustering to analyze mental disorders?", "output": [["How does the Brain Network Transformer leverage connection profiles and orthonormal clustering to analyze mental disorders?", "Title: Noncompact, Gauge-Invariant Simulations of U(1), SU(2), and SU(3)\n\nAbstract: We have applied a new noncompact, gauge-invariant, Monte Carlo method to simulate the U(1), SU(2), and SU(3) gauge theories on 8^4 and 12^4 lattices. For U(1) the Creutz ratios of the Wilson loops agree with the exact results for beta > 0.5 after a renormalization of the charge. The SU(2) and SU(3) Creutz ratios robustly display quark confinement at beta = 0.5 and beta = 2, respectively. At much weaker coupling, the SU(2) and SU(3) Creutz ratios agree with perturbation theory after a renormalization of the coupling constant. For SU(3) without quarks, our lattice QCD parameter is Lambda_L = 130 +- 18$ MeV."]], "corpus": "arxiv"}
 {"tstamp": 1722305896.3763, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722305894.1942, "finish": 1722305896.3763, "ip": "", "conv_id": "ebb67aaa6367404fada798faa16238be", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "can gravity waves travel faster than light?", "output": [["can gravity waves travel faster than light?", "Physics Stackexchange\n\nQ: Why can't the speed of gravitational waves be greater than the speed of light if the universe can expand faster than the speed of light? Since the expansion speed of the universe can be greater than the speed of light, why can't gravitational waves, which also uses space as the medium, travel faster than the speed of light?\n\nA: Gravitational waves are solutions to the linearized field equations\n$\\Box h_{\\mu \\nu} = 0,$\nwhere $\\Box$ is the d'Alembertian differential operator.\nThey are of the form\n\\begin{align}\nh_{\\mu \\nu}= \\text{Re}[H_{\\mu \\nu} e^{i k_{\\rho} x^{\\rho}}] , \n\\end{align}\nwhere $H_{\\mu \\nu}$ is just a complex symmetric matrix setting the polarization of the wave and $k_{\\rho}$ is its wave-vector.\nFor the above ansatz to satisfy the equations, we plug it into the equations to get the condition $k_{\\mu} k^{\\mu} =0$. This is just the statement that the wave-vector must be null, meaning the wave propagates at the speed of light.\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722305896.3763, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722305894.1942, "finish": 1722305896.3763, "ip": "", "conv_id": "0594d3384acf4283a4656c03b325081a", "model_name": "BM25", "prompt": "can gravity waves travel faster than light?", "output": [["can gravity waves travel faster than light?", "Physics Stackexchange\n\nQ: Why can't fermions be affected by effective gravity in non-linear quantum electrodynamics? Quantum electrodynamics based upon Euler-Heisenberg or Born-Infeld Lagrangians predict photons\nto move according to an effective metric which is dependent on the background electromagnetic\nfield. In other words, photon trajectories are curved in presence of electromagnetic fields,\nmeaning that an effective gravity is acting upon. If part of fermion masses is allegedly of\nelectromagnetic origin, the question why their trajectories are not affected by this\neffective gravity naturally comes to mind.\n\nA: In the presence of a background electromagnetic field, electromagnetic fields travel along a deformed light cone which is smaller than the \"relativistic light cone\". However, charged fermions can still travel faster than electromagnetic waves as long as they are still slower than the \"relativistic speed of light\". They emit Cherenkov radiation while doing so. \n"]], "corpus": "stackexchange"}
-{"tstamp": 1722306330.1591, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722306329.8317, "finish": 1722306330.1591, "ip": "", "conv_id": "5993407c26d04c54981a49217a38518d", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "when did scotland last qualify for world cup", "output": [["when did scotland last qualify for world cup", "Scotland national football team\n\nScotland lost just two of their first 43 international matches. It was not until a 2\u20130 home defeat by Ireland in 1903 that Scotland lost a match to a team other than England. This run of success meant that Scotland would have regularly topped the Elo ratings, which were calculated in 1998, between 1876 and 1904. Scotland won the British Home Championship outright on 24 occasions, and shared the title 17 times with at least one other team. A noteworthy victory for Scotland before the Second World War was the 5\u20131 victory over England in 1928, which led to that Scotland side being known as the \"Wembley Wizards\". Scotland played their first match outside the British Isles in 1929, beating Norway 7\u20133 in Bergen. Scotland continued to contest regular friendly matches against European opposition and enjoyed wins against Germany and France before losing to the Austrian \"Wunderteam\" and Italy in 1931.\nScotland, like the other Home Nations, did not enter the three FIFA World Cups held during the 1930s. This was because the four associations had been excluded from FIFA due to a disagreement regarding the status of amateur players. The four associations, including Scotland, returned to the FIFA fold after the Second World War. A match between a United Kingdom team and a \"Rest of the World\" team was played at Hampden Park in 1947 to celebrate this reconciliation.\n1950s: Entering the World Cup"]], "corpus": "wikipedia"}
 {"tstamp": 1722306330.1591, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722306329.8317, "finish": 1722306330.1591, "ip": "", "conv_id": "940948f56e9c4f5297cb17f69935bd8b", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "when did scotland last qualify for world cup", "output": [["when did scotland last qualify for world cup", "Scotland at the FIFA World Cup\n\nThe World Cup consists of two parts, the qualification phase and the final phase (officially called the World Cup Finals). The qualification phase, which currently take place over the three years preceding the Finals, is used to determine which teams qualify for the Finals. The current format of the Finals involves 32 teams competing at venues within the host nation (or nations) over a period of about a month. The World Cup Finals is the most widely viewed sporting event in the world, with an estimated 715.1 million people watching the 2006 tournament final.\nHistory\n1930s\nScotland did not compete in the first three World Cup competitions, in 1930, 1934 and 1938. Because of a dispute with FIFA over \"broken-time\" payments to players, the SFA, with the football associations of England, Ireland and Wales, withdrew from FIFA in 1928, and did not rejoin as a permanent member until 1946.\n1950s\nThe readmission of the Scottish Football Association to FIFA in 1946 meant that Scotland were now eligible to enter the 1950 FIFA World Cup. FIFA advised that places would be awarded to the top two teams in the 1949\u201350 British Home Championship, but the SFA announced that Scotland would attend the finals only if Scotland won the competition. Scotland won their first two matches, but a 1\u20130 home defeat by England meant that the Scots finished as runners-up. The SFA stood by this proclamation, despite pleas to the contrary by the Scotland players, supported by England captain Billy Wright and the other England players. Sportswriter Brian Glanville later described the SFA as showing \"baffling insularity and pique\" in their refusal to send a team to the World Cup."]], "corpus": "wikipedia"}
 {"tstamp": 1722306358.068, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722306357.4875, "finish": 1722306358.068, "ip": "", "conv_id": "be3fc968a3aa487ba8db65457d91fec3", "model_name": "text-embedding-3-large", "prompt": "Methods for tracing dataset provenance and legal implications in AI training data  ", "output": [["Methods for tracing dataset provenance and legal implications in AI training data  ", "Title: The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing & Attribution in AI\n\nAbstract: The race to train language models on vast, diverse, and inconsistently documented datasets has raised pressing concerns about the legal and ethical risks for practitioners. To remedy these practices threatening data transparency and understanding, we convene a multi-disciplinary effort between legal and machine learning experts to systematically audit and trace 1800+ text datasets. We develop tools and standards to trace the lineage of these datasets, from their source, creators, series of license conditions, properties, and subsequent use. Our landscape analysis highlights the sharp divides in composition and focus of commercially open vs closed datasets, with closed datasets monopolizing important categories: lower resource languages, more creative tasks, richer topic variety, newer and more synthetic training data. This points to a deepening divide in the types of data that are made available under different license conditions, and heightened implications for jurisdictional legal interpretations of copyright and fair use. We also observe frequent miscategorization of licenses on widely used dataset hosting sites, with license omission of 70%+ and error rates of 50%+. This points to a crisis in misattribution and informed use of the most popular datasets driving many recent breakthroughs. As a contribution to ongoing improvements in dataset transparency and responsible use, we release our entire audit, with an interactive UI, the Data Provenance Explorer, which allows practitioners to trace and filter on data provenance for the most popular open source finetuning data collections: www.dataprovenance.org."]], "corpus": "arxiv"}
 {"tstamp": 1722306358.068, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722306357.4875, "finish": 1722306358.068, "ip": "", "conv_id": "ff92d927e7a1440f91ca6ddae1fb40a5", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "Methods for tracing dataset provenance and legal implications in AI training data  ", "output": [["Methods for tracing dataset provenance and legal implications in AI training data  ", "Title: The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing & Attribution in AI\n\nAbstract: The race to train language models on vast, diverse, and inconsistently documented datasets has raised pressing concerns about the legal and ethical risks for practitioners. To remedy these practices threatening data transparency and understanding, we convene a multi-disciplinary effort between legal and machine learning experts to systematically audit and trace 1800+ text datasets. We develop tools and standards to trace the lineage of these datasets, from their source, creators, series of license conditions, properties, and subsequent use. Our landscape analysis highlights the sharp divides in composition and focus of commercially open vs closed datasets, with closed datasets monopolizing important categories: lower resource languages, more creative tasks, richer topic variety, newer and more synthetic training data. This points to a deepening divide in the types of data that are made available under different license conditions, and heightened implications for jurisdictional legal interpretations of copyright and fair use. We also observe frequent miscategorization of licenses on widely used dataset hosting sites, with license omission of 70%+ and error rates of 50%+. This points to a crisis in misattribution and informed use of the most popular datasets driving many recent breakthroughs. As a contribution to ongoing improvements in dataset transparency and responsible use, we release our entire audit, with an interactive UI, the Data Provenance Explorer, which allows practitioners to trace and filter on data provenance for the most popular open source finetuning data collections: www.dataprovenance.org."]], "corpus": "arxiv"}
@@ -51,7 +50,6 @@
 {"tstamp": 1722310277.0391, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722310276.8952, "finish": 1722310277.0391, "ip": "", "conv_id": "9776843ed00d4cab89c0894cad832500", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "what are chapters in the bible called?", "output": [["what are chapters in the bible called?", "Stackoverflow Stackexchange\n\nQ: How to strip executables thoroughly I'd like to strip as much as I can - on Linux: an ELF. I only want in there the stuff I need to run it.\nI tried using strip:\nstrip --strip-all elf\n\nBut it doesn't seem to do a good job: nm still displays lots of stuff, and the binary is still big.\nWhat should I do?\n\nA: I would check out this great article that goes into depth on making an ELF executable as small as possible. Maybe it has info that could help!\nhttp://www.muppetlabs.com/~breadbox/software/tiny/teensy.html\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722310277.0391, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722310276.8952, "finish": 1722310277.0391, "ip": "", "conv_id": "87810785bece4d6dab306293a64980f2", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "what are chapters in the bible called?", "output": [["what are chapters in the bible called?", "English Stackexchange\n\nQ: Why are the divisions of the Bible called \"verses\"? In common parlance, a verse is a writing arranged with a metrical rhythm, typically having a rhyme. Most of the books of Bible contain prose which do not follow metrical rhythm or rhyme. But their sentences are called verses. My question therefore is: Why are the divisions of the Bible called verses?\n\nA: A number of questions  on Stack Exchange - English Language and Usage have commented on the words 'stanza' and 'verse' in relation to poetry and to scripture.\n'Verses' are also used in Shakespeare.\nMerriam Webster defines a 'verse' in three ways : metrical verse, a stanza or a biblical verse.\nIn any work which requires to be referenced and quoted as part of its function, stanzas or verses are numbered accordingly so that they can be cited.\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722310317.0996, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722310316.7231, "finish": 1722310317.0996, "ip": "", "conv_id": "895db1e023ff4806920e918196f681a3", "model_name": "GritLM/GritLM-7B", "prompt": "who are the parents of the three fates", "output": [["who are the parents of the three fates", "Clotho\n\nAs one of the three fates her influence in Greek mythology was significant. Along with her sisters and Hermes, Clotho was given credit for creating the alphabet for their people. Even though Clotho and her sisters were worshiped as goddesses, their representation of fate is more central to their role in mythology. Thread represented human life and her decisions represented the fate of all people in society.\nOrigin\nAlthough there does not seem to be an epic tale in Classical Greek mythology in which the Fates are the main focus, they have played critical roles in the lives of deities and mortals. It is likely that the versions of myths about their primal origins were lost in the transitions from prehistoric Greek mythology to that of the Classical writings.\nAccording to Hesiod's Theogony, Clotho and her sisters (Atropos and Lachesis) were the daughters of Nyx (Night), without the assistance of a father. Later in the same work (ll. 901-906) they are said to have been born of Zeus and Themis. Clotho is mentioned in the tenth book of the Republic of Plato as the daughter of Necessity, as well.\nIn Roman mythology, it was believed that Clotho was the daughter of Uranus and Gaia.\nThe Ivory Shoulder\nAs one of the Three Fates, Clotho participated in creating the alphabet with Hermes, forced the goddess Aphrodite into making love with other gods, weakened the monster Typhon with poison fruit, persuaded Zeus to kill Asclepius with a bolt of lightning, and aided the deities in their war with the Giants by killing Agrius and Thoas with bronze clubs."]], "corpus": "wikipedia"}
-{"tstamp": 1722310317.0996, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722310316.7231, "finish": 1722310317.0996, "ip": "", "conv_id": "190f215de1614b968c5d2a87d0375480", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "who are the parents of the three fates", "output": [["who are the parents of the three fates", "Fates\n\nThe Fates are a common motif in European polytheism, most frequently represented as a trio of goddesses. The Fates shape the destiny of each human, often expressed in textile metaphors such as spinning fibers into yarn, or weaving threads on a loom. The trio are generally conceived of as sisters and are often given the names Clotho, Lachesis, and Atropos, which are the names of the Moirai, the version of the Fates who appear in Greek mythology. These divine figures are often artistically depicted as beautiful maidens with consideration to their serious responsibility: the life of mortals. Poets, on the other hand, typically express the Fates as ugly and unwavering, representing the gravity of their role within the mythological and human worlds."]], "corpus": "wikipedia"}
 {"tstamp": 1722310335.4722, "task_type": "retrieval", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722310335.2095, "finish": 1722310335.4722, "ip": "", "conv_id": "256e5bb6399a4ba7b599c9ba9a402e95", "model_name": "embed-english-v3.0", "prompt": "Learning word representations to improve IR tasks like query expansion and classification", "output": [["Learning word representations to improve IR tasks like query expansion and classification", "Title: Relevance-based Word Embedding\n\nAbstract: Learning a high-dimensional dense representation for vocabulary terms, also known as a word embedding, has recently attracted much attention in natural language processing and information retrieval tasks. The embedding vectors are typically learned based on term proximity in a large corpus. This means that the objective in well-known word embedding algorithms, e.g., word2vec, is to accurately predict adjacent word(s) for a given word or context. However, this objective is not necessarily equivalent to the goal of many information retrieval (IR) tasks. The primary objective in various IR tasks is to capture relevance instead of term proximity, syntactic, or even semantic similarity. This is the motivation for developing unsupervised relevance-based word embedding models that learn word representations based on query-document relevance information. In this paper, we propose two learning models with different objective functions; one learns a relevance distribution over the vocabulary set for each query, and the other classifies each term as belonging to the relevant or non-relevant class for each query. To train our models, we used over six million unique queries and the top ranked documents retrieved in response to each query, which are assumed to be relevant to the query. We extrinsically evaluate our learned word representation models using two IR tasks: query expansion and query classification. Both query expansion experiments on four TREC collections and query classification experiments on the KDD Cup 2005 dataset suggest that the relevance-based word embedding models significantly outperform state-of-the-art proximity-based embedding models, such as word2vec and GloVe."]], "corpus": "arxiv"}
 {"tstamp": 1722310335.4722, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722310335.2095, "finish": 1722310335.4722, "ip": "", "conv_id": "2f7dc574c8b94ae7a397af80c4cf05f5", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "Learning word representations to improve IR tasks like query expansion and classification", "output": [["Learning word representations to improve IR tasks like query expansion and classification", "Title: Relevance-based Word Embedding\n\nAbstract: Learning a high-dimensional dense representation for vocabulary terms, also known as a word embedding, has recently attracted much attention in natural language processing and information retrieval tasks. The embedding vectors are typically learned based on term proximity in a large corpus. This means that the objective in well-known word embedding algorithms, e.g., word2vec, is to accurately predict adjacent word(s) for a given word or context. However, this objective is not necessarily equivalent to the goal of many information retrieval (IR) tasks. The primary objective in various IR tasks is to capture relevance instead of term proximity, syntactic, or even semantic similarity. This is the motivation for developing unsupervised relevance-based word embedding models that learn word representations based on query-document relevance information. In this paper, we propose two learning models with different objective functions; one learns a relevance distribution over the vocabulary set for each query, and the other classifies each term as belonging to the relevant or non-relevant class for each query. To train our models, we used over six million unique queries and the top ranked documents retrieved in response to each query, which are assumed to be relevant to the query. We extrinsically evaluate our learned word representation models using two IR tasks: query expansion and query classification. Both query expansion experiments on four TREC collections and query classification experiments on the KDD Cup 2005 dataset suggest that the relevance-based word embedding models significantly outperform state-of-the-art proximity-based embedding models, such as word2vec and GloVe."]], "corpus": "arxiv"}
 {"tstamp": 1722310364.3913, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722310363.9154, "finish": 1722310364.3913, "ip": "", "conv_id": "8c01a7990c7d433c8f9c9a38ccfd45b3", "model_name": "voyage-multilingual-2", "prompt": "who starred in the film far from the madding crowd", "output": [["who starred in the film far from the madding crowd", "Far from the Madding Crowd (2015 film)\n\nPlot The film is set in 1870 in Britain. While working on her aunt's farm in Dorset, Bathsheba Everdene (Carey Mulligan) meets neighbouring farmer, Gabriel Oak (Matthias Schoenaerts). He eventually proposes, but the headstrong Bathsheba declines, saying she is too independent. One night, Gabriel's new sheepdog herds his entire flock off a steep cliff. Penniless, Gabriel leaves to search for work. In contrast, Bathsheba inherits an uncle's farm and leaves to run it.\nWhile at a fair trying to find employment, Gabriel sees recruiting officers. A girl there, Fanny Robbin, points out one soldier, Sergeant Frank Troy, her sweetheart. She suggests Gabriel seek employment at a farm in Weatherbury. Upon Gabriel's arrival, several buildings are on fire. He saves the barn from destruction. At dawn the next day, he meets the farm's new mistress, Bathsheba, who hires him as a shepherd.\nWhile in town trading her seed, Bathsheba sees her neighbour William Boldwood (Michael Sheen), a prosperous and mature bachelor. Bathsheba sends Boldwood a Valentine as a joke. He is both offended and intrigued. In the meantime, Fanny goes to the wrong church for her wedding; Troy, believing he's been jilted, is devastated. Boldwood comes to speak to Bathsheba, sees her handling sheep, and is charmed. He invites her to his home, where he proposes marriage. Bathsheba delays giving him a final answer, and Gabriel admonishes her for toying with Boldwood's affections. Stung by the criticism, she fires him. The next day, a crisis arises with the sheep that only Gabriel can manage. She successfully persuades him to return."]], "corpus": "wikipedia"}
@@ -82,7 +80,6 @@
 {"tstamp": 1722311121.1601, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722311119.0323, "finish": 1722311121.1601, "ip": "", "conv_id": "af5d587baf874dcaa68ca13be655b9ec", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "Impact of AI chatbots on academic integrity and plagiarism", "output": [["Impact of AI chatbots on academic integrity and plagiarism", "Title: Will ChatGPT get you caught? Rethinking of Plagiarism Detection\n\nAbstract: The rise of Artificial Intelligence (AI) technology and its impact on education has been a topic of growing concern in recent years. The new generation AI systems such as chatbots have become more accessible on the Internet and stronger in terms of capabilities. The use of chatbots, particularly ChatGPT, for generating academic essays at schools and colleges has sparked fears among scholars. This study aims to explore the originality of contents produced by one of the most popular AI chatbots, ChatGPT. To this end, two popular plagiarism detection tools were used to evaluate the originality of 50 essays generated by ChatGPT on various topics. Our results manifest that ChatGPT has a great potential to generate sophisticated text outputs without being well caught by the plagiarism check software. In other words, ChatGPT can create content on many topics with high originality as if they were written by someone. These findings align with the recent concerns about students using chatbots for an easy shortcut to success with minimal or no effort. Moreover, ChatGPT was asked to verify if the essays were generated by itself, as an additional measure of plagiarism check, and it showed superior performance compared to the traditional plagiarism-detection tools. The paper discusses the need for institutions to consider appropriate measures to mitigate potential plagiarism issues and advise on the ongoing debate surrounding the impact of AI technology on education. Further implications are discussed in the paper."]], "corpus": "arxiv"}
 {"tstamp": 1722311121.1601, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722311119.0323, "finish": 1722311121.1601, "ip": "", "conv_id": "6912b24d5d0444babee7fd61715355bc", "model_name": "text-embedding-004", "prompt": "Impact of AI chatbots on academic integrity and plagiarism", "output": [["Impact of AI chatbots on academic integrity and plagiarism", "Title: Will ChatGPT get you caught? Rethinking of Plagiarism Detection\n\nAbstract: The rise of Artificial Intelligence (AI) technology and its impact on education has been a topic of growing concern in recent years. The new generation AI systems such as chatbots have become more accessible on the Internet and stronger in terms of capabilities. The use of chatbots, particularly ChatGPT, for generating academic essays at schools and colleges has sparked fears among scholars. This study aims to explore the originality of contents produced by one of the most popular AI chatbots, ChatGPT. To this end, two popular plagiarism detection tools were used to evaluate the originality of 50 essays generated by ChatGPT on various topics. Our results manifest that ChatGPT has a great potential to generate sophisticated text outputs without being well caught by the plagiarism check software. In other words, ChatGPT can create content on many topics with high originality as if they were written by someone. These findings align with the recent concerns about students using chatbots for an easy shortcut to success with minimal or no effort. Moreover, ChatGPT was asked to verify if the essays were generated by itself, as an additional measure of plagiarism check, and it showed superior performance compared to the traditional plagiarism-detection tools. The paper discusses the need for institutions to consider appropriate measures to mitigate potential plagiarism issues and advise on the ongoing debate surrounding the impact of AI technology on education. Further implications are discussed in the paper."]], "corpus": "arxiv"}
 {"tstamp": 1722311138.467, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722311136.1656, "finish": 1722311138.467, "ip": "", "conv_id": "c96bbb99cf3546ba97838b73048bd5fa", "model_name": "BM25", "prompt": "What are the main causes and effects of the Industrial Revolution?", "output": [["What are the main causes and effects of the Industrial Revolution?", "Industrial Revolution\n\nPrimitivism Primitivism argues that the Industrial Revolution have created an un-natural frame of society and the world in which humans need to adapt to an un-natural urban landscape in which humans are perpetual cogs without personal autonomy.\nCertain primitivists argue for a return to pre-industrial society, while others argue that technology such as modern medicine, and agriculture are all positive for humanity assuming they are controlled by and serve humanity and have no effect on the natural environment.\nPollution and ecological collapse\nThe Industrial Revolution has been criticised for leading to immense ecological and habitat destruction. It has led to immense decrease in the biodiversity of life on Earth. The Industrial revolution has been said to be inherently unsustainable and will lead to eventual collapse of society, mass hunger, starvation, and resource scarcity.\nThe Anthropocene\nThe Anthropocene is a proposed epoch or mass extinction coming from humanity (anthropo- is the Greek root for humanity). Since the start of the Industrial revolution humanity has permanently changed the Earth, such as immense decrease in biodiversity, and mass extinction caused by the Industrial revolution. The effects include permanent changes to the Earth's atmosphere and soil, forests, the mass destruction of the Industrial revolution has led to catastrophic impacts on the Earth. Most organisms are unable to adapt leading to mass extinction with the remaining undergoing evolutionary rescue, as a result of the Industrial revolution."]], "corpus": "wikipedia"}
-{"tstamp": 1722311138.467, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722311136.1656, "finish": 1722311138.467, "ip": "", "conv_id": "babef639f1a24f2a9ac89cb3279d6177", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "What are the main causes and effects of the Industrial Revolution?", "output": [["What are the main causes and effects of the Industrial Revolution?", "De-industrialisation of India\n\nThe fall in the hegemony of Mughals reduced the overall productivity of agriculture and reduced the supply of grains. The grain was the primary consumption good for the Indian workers and was non-tradeable. The reduction in the supply of grain resulted in the rise of its prices. This rise in prices and negative supply shock led to a rise in the nominal wages in the cotton and weaving industry. The increased competition from British cotton and rising nominal wages reduced the profitability of the cotton industry of India. Thus, the negative supply shock in agricultural production is also an important reason behind the de-industrialisation of cotton\u2013industries.\nThe short run as well as long run impact on living standards and growth rate of GDP providing agriculture sector competitive advantage with strengthening of the productivity advance on the land at home or increasing openness to world in turn increases GDP in the short run. The causes of de-industrialisation are region or country specific as in the case of India in the 19th and 20th century. The colonial rule under the British led to the decline of textile and handicrafts industries through their policies and introduction of machine made goods in to the Indian market. Some of the causes of de-industrialisation in India during that period were:\nIntroduction of machine made goods in the Indian subcontinent at a cheaper rate, which led to the decline of the traditional textile industry of India."]], "corpus": "wikipedia"}
 {"tstamp": 1722311164.9982, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722311164.8544, "finish": 1722311164.9982, "ip": "", "conv_id": "92e783c936084f088d8757c497e00510", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "are electrical outlets on studs?", "output": [["are electrical outlets on studs?", "Electronics Stackexchange\n\nQ: Purpose of breadboard \"studs\" on base off micro breadboards? I recently bought some micro breadboards on Ebay.  Extremely useful little things.  However each have two awkward studs on their bases.  See the attached photo.   What I'm curious about is their purpose?  What might these \"plugin\" to or be used for?  I think I'm going to file them off.\n\n\nA: They connect to a base that fixes them with regards to each other.\n\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722311164.9982, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722311164.8544, "finish": 1722311164.9982, "ip": "", "conv_id": "5a38498ae0f7449186feb5beb2f13faf", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "are electrical outlets on studs?", "output": [["are electrical outlets on studs?", "Math Stackexchange\n\nQ: Write $\\cos^2(x)$ as linear combination of $x \\mapsto \\sin(x)$ and $x \\mapsto \\cos(x)$ \nCan we write $\\cos^2(x)$ as linear combination of $x \\mapsto \\sin(x)$ and  $x \\mapsto \\cos(x)$?\n\nI know\n$$\n\\cos^2(x)\n= \\frac{\\cos(2x) + 1}{2}\n= 1 - \\sin^2(x)\n= \\cos(2x) + \\sin^2(x)\n$$\nbut none of these helped.\nThen, I tried to solve\n$$\n\\cos^2(x) = \\alpha \\sin(x) + \\beta \\cos(x)\n$$\nfor the coefficients $\\alpha, \\beta \\in \\mathbb{R}$.\nBut when plugging in $x = 0$ I get $\\beta = 1$ and for $x = \\frac{\\pi}{2}$ I get $\\alpha = 0$. Plugging those values back in I obtain a false statement, and WolframAlpha can't do better!\nThis is from a numerical analysis exam and the second function is $x \\mapsto \\sqrt{2}\\cos\\left(\\frac{\\pi}{4} - x \\right)$, which can easily be expressed in terms of $x \\mapsto \\sin(x)$ and $x \\mapsto \\cos(x)$ by the corresponding addition formula.\n\nA: The function $f(x):=\\cos^2 x$ has $f(x+\\pi)\\equiv f(x)$, but any linear combination $g$ of $\\cos$ and $\\sin$ has $g(x+\\pi)\\equiv -g(x)$.\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722311174.9589, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722311174.5805, "finish": 1722311174.9589, "ip": "", "conv_id": "344ba32a2d4e44d4857f9fcc603f8ef8", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "What are the differences between machine learning and deep learning?", "output": [["What are the differences between machine learning and deep learning?", "Comparison of deep learning software\n\nThe following table compares notable software frameworks, libraries and computer programs for deep learning.\nDeep-learning software by name\nComparison of compatibility of machine learning models"]], "corpus": "wikipedia"}

data/retrieval_individual-cc11c9ee-fc93-4cd6-a4b5-246a7f21038c.jsonl

@@ -12,13 +12,11 @@
 {"tstamp": 1722322685.4922, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722322684.6919, "finish": 1722322685.4922, "ip": "", "conv_id": "7f6b33e7c9074fd4a1cae2f02ba4f4ed", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "Who is Napoleon?", "output": [["Who is Napoleon?", "Napoleon\n\nNapoleon Bonaparte (born Napoleone di Buonaparte; 15 August 1769 \u2013 5 May 1821), later known by his regnal name Napoleon\u00a0I, was a French military and political leader who rose to prominence during the French Revolution and led a series of successful campaigns across Europe during the Revolutionary Wars and Napoleonic Wars from 1796 to 1815. He was the leader of the French Republic as First Consul from 1799 to 1804, then of the French Empire as Emperor of the French from 1804 to 1814, and briefly again in 1815.\nBorn on the island of Corsica to a family of Italian origin, Napoleon moved to mainland France in 1779 and was commissioned as an officer in the French Army in 1785. He supported the French Revolution in 1789, and promoted its cause in Corsica. He rose rapidly in the ranks after breaking the siege of Toulon in 1793 and firing on royalist insurgents in Paris on 13 Vend\u00e9miaire in 1795. In 1796, Napoleon commanded a military campaign against the Austrians and their Italian allies in the War of the First Coalition, scoring decisive victories and becoming a national hero. He led an expedition to Egypt and Syria in 1798 which served as a springboard to political power. In November 1799, Napoleon engineered the Coup of 18 Brumaire against the Directory, and became First Consul of the Republic. He won the Battle of Marengo in 1800, which secured French victory in the War of the Second Coalition, and in 1803 sold the territory of Louisiana to the United States, which doubled the latter's area. In December 1804, Napoleon crowned himself Emperor of the French, further expanding his power."]], "corpus": "wikipedia"}
 {"tstamp": 1722322748.8081, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722322748.6044, "finish": 1722322748.8081, "ip": "", "conv_id": "02079a96a50c46ba9f98c2be3ba5f4e0", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "Who is Napoleon II?", "output": [["Who is Napoleon II?", "Napoleon II\n\nNapoleon II (Napol\u00e9on Fran\u00e7ois Joseph Charles Bonaparte; 20 March 181122 July 1832) was the disputed Emperor of the French for a few weeks in 1815. He was the son of Emperor Napoleon I and Empress Marie Louise, daughter of Emperor Francis I of Austria. Napoleon II had been Prince Imperial of France and King of Rome since birth. After the fall of his father, he lived the rest of his life in Vienna and was known in the Austrian court as Franz, Duke of Reichstadt for his adult life (from the German version of his second given name, along with a title his grandfather granted him in 1818).  He was posthumously given the nickname L'Aiglon (\"the Eaglet\")."]], "corpus": "wikipedia"}
 {"tstamp": 1722322780.9411, "task_type": "retrieval", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722322780.5795, "finish": 1722322780.9411, "ip": "", "conv_id": "c292b9c11c1842058e186af5f38441ba", "model_name": "embed-english-v3.0", "prompt": "Who is the disputed Emperor of the French?", "output": [["Who is the disputed Emperor of the French?", "Emperor of the French\n\nEmperor of the French (French: Empereur des Fran\u00e7ais) was the title of the monarch and supreme ruler of the First and the Second French Empires.\nDetails\nA title and office used by the House of Bonaparte starting when Napoleon Bonaparte was proclaimed Emperor on 18 May 1804 by the Senate and was crowned Emperor of the French on 2 December 1804 at the cathedral of Notre-Dame de Paris, in Paris, with the Crown of Napoleon."]], "corpus": "wikipedia"}
-{"tstamp": 1722322808.6647, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722322808.3331, "finish": 1722322808.6647, "ip": "", "conv_id": "a8361e48a2134bafa6135be25d8ad87d", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "What is Sentence Transformers?", "output": [["What is Sentence Transformers?", "BERT (language model)\n\nBidirectional Encoder Representations from Transformers (BERT) is a language model based on the transformer architecture, notable for its dramatic improvement over previous state of the art models. It was introduced in October 2018 by researchers at Google. A 2020 literature survey concluded that \"in a little over a year, BERT has become a ubiquitous baseline in Natural Language Processing (NLP) experiments counting over 150 research publications analyzing and improving the model.\"\nBERT was originally implemented in the English language at two model sizes: (1) BERTBASE: 12 encoders with 12 bidirectional self-attention heads totaling 110 million parameters, and (2) BERTLARGE: 24 encoders with 16 bidirectional self-attention heads totaling 340 million parameters. Both models were pre-trained on the Toronto BookCorpus (800M words) and English Wikipedia  (2,500M words).\nDesign\nBERT is an \"encoder-only\" transformer architecture."]], "corpus": "wikipedia"}
 {"tstamp": 1722322808.6647, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722322808.3331, "finish": 1722322808.6647, "ip": "", "conv_id": "a162df51a68a4151bf8a4c558811e179", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "What is Sentence Transformers?", "output": [["What is Sentence Transformers?", "Transformers\n\nIn 1986, the cartoon became the film The Transformers: The Movie, which is set in the year 2005. It introduced the Matrix as the \"Autobot Matrix of Leadership\", as a fatally wounded Prime gives it to Ultra Magnus; however, as Prime dies he drops the matrix, which is then caught by Hot Rod who subsequently becomes Rodimus Prime later on in the film. Unicron, a Transformer who devours planets, fears its power and re-creates a heavily damaged Megatron as Galvatron, as well as Bombshell or Skywarp becoming Cyclonus, Thundercracker becoming Scourge and two other Insecticons becoming Scourge's huntsmen, the Sweeps. Eventually, Rodimus Prime takes out the Matrix and destroys Unicron. In the United Kingdom, the weekly comic book interspliced original material to keep up with U.S. reprints, and The Movie provided much new material. Writer Simon Furman proceeded to expand the continuity with movie spin-offs involving the time travelling Galvatron. The Movie also featured guest voices from Leonard Nimoy as Galvatron, Scatman Crothers as Jazz, Casey Kasem as Cliffjumper, Orson Welles as Unicron and Eric Idle as the leader of the Junkions (Wreck-Gar, though unnamed in the movie). The Transformers theme tune for the film was performed by Lion with \"Weird Al\" Yankovic adding a song to the soundtrack."]], "corpus": "wikipedia"}
 {"tstamp": 1722322853.5078, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722322853.1126, "finish": 1722322853.5078, "ip": "", "conv_id": "7f876a2cc06144d8bccbdef351f800f2", "model_name": "GritLM/GritLM-7B", "prompt": "Chicaco attacks", "output": [["Chicaco attacks", "USS Chicago (CA-29)\n\nWorld War II When the Japanese attacked Pearl Harbor on 7 December 1941, Chicago was at sea with Task Force 12 and the Force immediately began a five-day sweep in the Oahu-Johnston-Palmyra triangle in an effort to intercept the enemy. The Force returned to Pearl Harbor on 12 December; from 14\u201327 December, Chicago operated with Task Force 11 on patrol and search missions.\nOn 2 February 1942, Chicago departed Pearl Harbor for Suva, where she joined the newly formed ANZAC Squadron, later redesignated as Task Force 44. During March and April, the cruiser operated off the Louisiade Archipelago, covering the attacks on Lae and Salamaua, New Guinea. In a position to intercept enemy surface units which attempted to attack Port Moresby, Chicago also provided cover for the arrival of American troops on New Caledonia.\nOn 1 May, Chicago was ordered from Noum\u00e9a to join Commander, Southwest Pacific, and on the 4th she supported  in her strike against the Japanese on Tulagi, Solomon Islands during the Battle of the Coral Sea. On 7 May, she proceeded, with the Support Group, to intercept and attack the Japanese Port Moresby invasion group. The following day, the group underwent several Japanese air attacks, during which Chicago suffered several casualties from strafing, but drove off the planes and proceeded ahead until it was clear that the Japanese force had been turned back."]], "corpus": "wikipedia"}
 {"tstamp": 1722322853.5078, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722322853.1126, "finish": 1722322853.5078, "ip": "", "conv_id": "39e24b7acfbc433fbe17b9aaebd9b23b", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "Chicaco attacks", "output": [["Chicaco attacks", "Chiapas conflict\n\n6 July 2003: Violent acts take place during the legislative elections in indigenous regions of Chiapas, principally in San Juan Cancuc, Zinacant\u00e1n and Chenalh\u00f3. At the federal level, the largest rate of absenteeism was registered in the recent history of the country.\nSeptember/October 2003: A series of conflicts between members of the Independent Center of Agricultural Workers and Campesinos (CIOAC) and Zapatistas, around the detention of Arm\u00edn Morales Jim\u00e9nez by militants of the EZLN for the accused crime of abuse of confidence.\n22 January 2004: The houses of the community of Nuevo San Rafael in Montes Azules Reserve were all burned. According to the Secretary of Agrarian Reform (SRA), the inhabitants had voluntarily decided to abandon their homes and return to their places of origin. NGOs accused the SRA of having divided the population so as to force residents to leave the reserve.\n10 April 2004: Zapatista supporters from the municipality of Zinacant\u00e1n were ambushed by members of the PRD, leaving dozens wounded and displacing 125 Zapatista families.\n23 April 2004: Noel P\u00e1vel Gonz\u00e1lez, student at the National Autonomous University of Mexico and the National School of Anthropology and History, was found murdered in Mexico City. Elements of the investigation point towards the involvement of the ultra-right group \"El Yunque.\""]], "corpus": "wikipedia"}
 {"tstamp": 1722322898.0607, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722322897.8496, "finish": 1722322898.0607, "ip": "", "conv_id": "830fdba8c1114ad692ade33324a03c62", "model_name": "BM25", "prompt": "What is Hugging Face?", "output": [["What is Hugging Face?", "X-23\n\nAfter being found by Kitty, Laura was taken to their base to recover. When Laura wakes up, with her memories now returning, at the former Weapon X facility she immediately tries to escape. Teen Cyclops goes to talk to her, while he's gone Teen Jean mentions to the team that Teen Cyclops \"fancies\" Laura. As Laura makes it outside she's met by Teen Cyclops, who was waiting for her. Teen Cyclops tries to calm her down but Laura only ceases hostility after getting Teen Cyclops scent and figuring out that Teen Cyclops is who he says he is.\nThe two talk, with Laura telling him that she does not want to talk about her past or what happened to her. Suddenly Teen Cyclops awkwardly hugs her, when she was distracted and looking sad, because he thought she needed a hug. Laura tells him that she does not do hugs, especially not with an audience as Kitty, Teen Jean, and Teen Beast were watching them. Teen Jean expressed a very conflicted look upon her face due to the hug and having read part of Laura's mind, thus she knows what happened in Avengers Arena. Laura thanks them for helping her and suggests they take the fight to the Purifiers, but in order for her to track them down, they will have to return her to where they found her."]], "corpus": "wikipedia"}
 {"tstamp": 1722322898.0607, "task_type": "retrieval", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722322897.8496, "finish": 1722322898.0607, "ip": "", "conv_id": "395cfbc72eed40159ded5a02938adc32", "model_name": "embed-english-v3.0", "prompt": "What is Hugging Face?", "output": [["What is Hugging Face?", "Hug\n\nEtymology The origins of the word are unknown, but two hypotheses exist. The first is that the verb \"hug\" (first used in the 1560s) could be related to the Old Norse word , which meant to comfort. The second hypothesis is that the word is related to the German word , which means to foster or cherish, and originally meant to enclose with a hedge.\nCharacteristics\nA hug, sometimes in association with a kiss, is a form of nonverbal communication. Depending on culture, context and relationship, a hug can indicate familiarity, love, affection, friendship, brotherhood, flirting or sympathy.\nHugs can indicate support, comfort, and consolation, particularly where words are insufficient. A hug usually demonstrates affection and emotional warmth, sometimes arising from joy or happiness when reunited with someone or seeing someone absent after a long time. A non-reciprocal hug may demonstrate a relational problem.\nA hug can range from a brief one-second squeeze, with the arms not fully around the other person, to an extended holding. The length of a hug in any situation is socially and culturally determined. In the case of lovers, and occasionally others, the hips may also be pressed together. The emotionality of the hug can also influence the direction of the hug.\nThe direction of hugs generally corresponds with handedness, with right-handed people tending to lead with the right hand, but a heightened emotional state makes it slightly more likely for people to lead with the left hand. This small but significant effect has been attributed to right-hemispheric emotional processing."]], "corpus": "wikipedia"}
-{"tstamp": 1722322926.4452, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722322926.2649, "finish": 1722322926.4452, "ip": "", "conv_id": "49aa9d1fb4e54f14b66643bdec0e311a", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "What is NLTK?", "output": [["What is NLTK?", "Computational linguistics\n\nComputational linguistics is an interdisciplinary field concerned with the computational modelling of natural language, as well as the study of appropriate computational approaches to linguistic questions. In general, computational linguistics draws upon linguistics, computer science, artificial intelligence, mathematics, logic, philosophy, cognitive science, cognitive psychology, psycholinguistics, anthropology and neuroscience, among others.\nOrigins\nThe field overlapped with artificial intelligence since the efforts in the United States in the 1950s to use computers to automatically translate texts from foreign languages, particularly Russian scientific journals, into English. Since rule-based approaches were able to make arithmetic (systematic) calculations much faster and more accurately than humans, it was expected that lexicon, morphology, syntax and semantics can be learned using explicit rules, as well. After the failure of rule-based approaches, David Hays coined the term in order to distinguish the field from AI and co-founded both the Association for Computational Linguistics (ACL) and the International Committee on Computational Linguistics (ICCL) in the 1970s and 1980s. What started as an effort to translate between languages evolved into a much wider field of natural language processing.\nAnnotated corpora\nIn order to be able to meticulously study the English language, an annotated text corpus was much  needed. The Penn Treebank was one of the most used corpora. It consisted of IBM computer manuals, transcribed telephone conversations, and other texts, together containing over 4.5 million words of American English, annotated using both  part-of-speech tagging and syntactic bracketing."]], "corpus": "wikipedia"}
 {"tstamp": 1722322926.4452, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722322926.2649, "finish": 1722322926.4452, "ip": "", "conv_id": "340198e89e784b64b1d79d0e65500135", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "What is NLTK?", "output": [["What is NLTK?", "Natural Language Toolkit\n\nThe Natural Language Toolkit, or more commonly NLTK, is a suite of libraries and programs for symbolic and statistical natural language processing (NLP) for English written in the Python programming language. It supports classification, tokenization, stemming, tagging, parsing, and semantic reasoning functionalities. It was developed by Steven Bird and Edward Loper in the Department of Computer and Information Science at the University of Pennsylvania. NLTK includes graphical demonstrations and sample data. It is accompanied by a book that explains the underlying concepts behind the language processing tasks supported by the toolkit, plus a cookbook.\nNLTK is intended to support research and teaching in NLP or closely related areas, including empirical linguistics, cognitive science, artificial intelligence, information retrieval, and machine learning.\nNLTK has been used successfully as a teaching tool, as an individual study tool, and as a platform for prototyping and building research systems. There are 32 universities in the US and 25 countries using NLTK in their courses.\nLibrary highlights\nDiscourse representation\nLexical analysis: Word and text tokenizer\nn-gram and collocations\nPart-of-speech tagger\nTree model and Text chunker for capturing"]], "corpus": "wikipedia"}
 {"tstamp": 1722322990.8571, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722322990.4313, "finish": 1722322990.8571, "ip": "", "conv_id": "904708d39d7f40d69ba876b5f826d941", "model_name": "GritLM/GritLM-7B", "prompt": "What is the tallest peak in Warren Range, Antarctica?", "output": [["What is the tallest peak in Warren Range, Antarctica?", "Vinson Massif\n\nMount Vinson is the highest peak in Antarctica, at . It lies in the north part of Vinson Massif's summit plateau in the south portion of the main ridge of the Sentinel Range about  north of Hollister Peak. It was first climbed in 1966 by an American team led by Nicholas Clinch. An expedition in 2001 was the first to climb via the Eastern route, and also took GPS measurements of the height of the peak. As of February 2010, 1,400 climbers have attempted to reach the summit of Mount Vinson. Mount Vinson is ranked 6th by topographic isolation.\nGeography\nThe Vinson Massif extends between Goodge Col and Branscomb Glacier to the northwest, Nimitz Glacier and Gildea Glacier to the southwest and south, and Dater Glacier and its tributary Hinkley Glacier to the east. The southeastern part of the massif ends at Hammer Col, which joins it to the Craddock Massif, of which the highest point is Mount Rutford (). The massif comprises both the high central Vinson Plateau with its few peaks rising to over , and several side ridges mostly trending southwest or northeast from the plateau.\nThe current height () of Mount Vinson was measured by a GPS survey that was conducted by the 2004 Omega Foundation team comprising Damien Gildea of Australia (leader) and Rodrigo Fica and Camilo Rada of Chile. Since 1998 and continuing through 2007, the Omega Foundation has placed a GPS receiver on the summit for a suitable period of time to obtain accurate satellite readings."]], "corpus": "wikipedia"}
 {"tstamp": 1722322990.8571, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722322990.4313, "finish": 1722322990.8571, "ip": "", "conv_id": "a21aee4127f6494db3edb0f70eebdbc1", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "What is the tallest peak in Warren Range, Antarctica?", "output": [["What is the tallest peak in Warren Range, Antarctica?", "Vinson Massif\n\nMount Vinson is the highest peak in Antarctica, at . It lies in the north part of Vinson Massif's summit plateau in the south portion of the main ridge of the Sentinel Range about  north of Hollister Peak. It was first climbed in 1966 by an American team led by Nicholas Clinch. An expedition in 2001 was the first to climb via the Eastern route, and also took GPS measurements of the height of the peak. As of February 2010, 1,400 climbers have attempted to reach the summit of Mount Vinson. Mount Vinson is ranked 6th by topographic isolation.\nGeography\nThe Vinson Massif extends between Goodge Col and Branscomb Glacier to the northwest, Nimitz Glacier and Gildea Glacier to the southwest and south, and Dater Glacier and its tributary Hinkley Glacier to the east. The southeastern part of the massif ends at Hammer Col, which joins it to the Craddock Massif, of which the highest point is Mount Rutford (). The massif comprises both the high central Vinson Plateau with its few peaks rising to over , and several side ridges mostly trending southwest or northeast from the plateau.\nThe current height () of Mount Vinson was measured by a GPS survey that was conducted by the 2004 Omega Foundation team comprising Damien Gildea of Australia (leader) and Rodrigo Fica and Camilo Rada of Chile. Since 1998 and continuing through 2007, the Omega Foundation has placed a GPS receiver on the summit for a suitable period of time to obtain accurate satellite readings."]], "corpus": "wikipedia"}
@@ -29,7 +27,6 @@
 {"tstamp": 1722323177.8769, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722323177.4593, "finish": 1722323177.8769, "ip": "", "conv_id": "f0684e70908c49fbbd04e82653771ae6", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "Who did Cleopatra have a baby with?", "output": [["Who did Cleopatra have a baby with?", "Caesarion\n\nPtolemy XV Caesar (; , ; 23 June 47 BC \u2013 29 August 30 BC), nicknamed Caesarion (, , \"Little Caesar\"), was the last pharaoh of Ptolemaic Egypt, reigning with his mother Cleopatra VII from 2 September 44 BC until her death by 12 August 30 BC, then as sole ruler until his death was ordered by Octavian (who would become the first Roman emperor as Augustus).\nCaesarion was the eldest son of Cleopatra and the only known biological son of Julius Caesar, after whom he was named. He was the last sovereign member of the Ptolemaic dynasty of Egypt."]], "corpus": "wikipedia"}
 {"tstamp": 1722323271.3011, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722323271.0615, "finish": 1722323271.3011, "ip": "", "conv_id": "00d652f1a43645d78978d938ceec2a54", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "What is Twitter?", "output": [["What is Twitter?", "Twitter\n\nX, commonly referred to by its former name Twitter, is a social networking service. It is one of the world's largest social media websites and the seventh-most visited website in the world. Users can share short text messages, images, and videos in posts (formerly \"tweets\") and like or repost/retweet other users' content. X also includes direct messaging, video and audio calling, bookmarks, lists and communities, and Spaces, a social audio feature. Users can vote on context added by approved users using the Community Notes feature.\nTwitter was created in March 2006 by Jack Dorsey, Noah Glass, Biz Stone, and Evan Williams, and was launched in July of that year. Twitter grew quickly; by 2012 more than 100 million users produced 340 million tweets per day. Twitter, Inc., was based in San Francisco, California, and had more than 25 offices around the world. A signature characteristic of the service is that posts are required to be brief. Posts were initially limited to 140 characters, which was changed to 280 characters in 2017 and removed for paid accounts in 2023. The majority of tweets are produced by a minority of users. In 2020, it was estimated that approximately 48 million accounts (15 percent of all accounts) were not genuine people."]], "corpus": "wikipedia"}
 {"tstamp": 1722323271.3011, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722323271.0615, "finish": 1722323271.3011, "ip": "", "conv_id": "04719f7eea0f496692e75d85656d0b47", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "What is Twitter?", "output": [["What is Twitter?", "Twitter\n\nX, commonly referred to by its former name Twitter, is a social networking service. It is one of the world's largest social media websites and the seventh-most visited website in the world. Users can share short text messages, images, and videos in posts (formerly \"tweets\") and like or repost/retweet other users' content. X also includes direct messaging, video and audio calling, bookmarks, lists and communities, and Spaces, a social audio feature. Users can vote on context added by approved users using the Community Notes feature.\nTwitter was created in March 2006 by Jack Dorsey, Noah Glass, Biz Stone, and Evan Williams, and was launched in July of that year. Twitter grew quickly; by 2012 more than 100 million users produced 340 million tweets per day. Twitter, Inc., was based in San Francisco, California, and had more than 25 offices around the world. A signature characteristic of the service is that posts are required to be brief. Posts were initially limited to 140 characters, which was changed to 280 characters in 2017 and removed for paid accounts in 2023. The majority of tweets are produced by a minority of users. In 2020, it was estimated that approximately 48 million accounts (15 percent of all accounts) were not genuine people."]], "corpus": "wikipedia"}
-{"tstamp": 1722323341.6208, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722323341.3702, "finish": 1722323341.6208, "ip": "", "conv_id": "c63e2ac5acd24904b31234b484497118", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "What province is Utrecht located in?", "output": [["What province is Utrecht located in?", "Randstad\n\nThe publication \"Randstad 2040\", released by the Government of the Netherlands, reveals that the Noordvleugel (Amsterdam and Utrecht region) is already growing parallel to the A1, A2, and A4 motorways, as well as into the northern part of North Holland (Alkmaar and Hoorn region) and Flevoland (Almere and Lelystad region). There is also a trend shown that the Noordvleugel is expanding parallel to the A2 (Amsterdam towards Eindhoven) and A12 (Utrecht towards the WERV region, encompassing the cities of Wageningen, Ede, Rhenen and Veenendaal) motorways. This region is called the Zuidoostflank (English: Southeastside).\nCulture\nIt is sometimes argued that a cultural divide exists between the Randstad and the rest of the country. This distinction is usually made in relation to Dutch politics and media, who according to critics are mostly interested in the affairs of the Randstad. Both branches (government and media) have their centre in the Randstad; respectively in The Hague and in Hilversum. The Randstad itself, however, does not represent a unified cultural zone. It is not a 'place' of residence or a carrier of cultural identity. According to the late influential urbanist Niek de Boer, the Randstad simply 'does not exist'. While the cities and landscapes in the Randstad share some commonalities, there are also large differences originating in centuries of divergent development. There are strong local identities within the region, especially in rural environments.\nPolitically, the Randstad is broadly right-leaning and conservative, with student cities, including Amsterdam, Utrecht, Haarlem, Leiden and Delft, being more left-leaning and progressive.\nThe Randstad is home to some of the most reputable universities in Europe, including the University of Amsterdam, the VU Amsterdam, Leiden University, Erasmus University Rotterdam, Utrecht University and the Delft University of Technology."]], "corpus": "wikipedia"}
 {"tstamp": 1722323376.2262, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722323375.9838, "finish": 1722323376.2262, "ip": "", "conv_id": "b979de4d9b3f432897f05ebeba192fed", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "how many blue stripes are on the american flag", "output": [["how many blue stripes are on the american flag", "Flag of the United States\n\nThe national flag of the United States, often referred to as the American flag or the U.S. flag, consists of thirteen equal horizontal stripes of red (top and bottom) alternating with white, with a blue rectangle in the canton, referred to as the union and bearing fifty small, white, five-pointed stars arranged in nine offset horizontal rows, where rows of six stars (top and bottom) alternate with rows of five stars. The 50 stars on the flag represent the 50 U.S. states, and the 13 stripes represent the thirteen British colonies that declared independence from Great Britain, which they went on to secure by their victory in the American Revolutionary War.\nNicknames for the flag include the Stars and Stripes, Old Glory, and the Star-Spangled Banner. Credit for the term \"Stars and Stripes\" has been given to the Marquis de Lafayette, a French soldier who volunteered his aid to the Continental Army, led by George Washington, in the Revolutionary War against Britain.\nHistory\nThe current design of the U.S. flag is its 27th; the design of the flag has been modified officially 26 times since 1777. The 48-star flag was in effect for 47 years until the 49-star version became official on July 4, 1959. The 50-star flag was ordered by then president Eisenhower on August 21, 1959, and was adopted in July 1960. It is the longest-used version of the U.S. flag and has been in use for over  years."]], "corpus": "wikipedia"}
 {"tstamp": 1722323376.2262, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722323375.9838, "finish": 1722323376.2262, "ip": "", "conv_id": "54ddc092badb403c8196a8a2f9b11dff", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "how many blue stripes are on the american flag", "output": [["how many blue stripes are on the american flag", "Flag of the United States\n\nThe national flag of the United States, often referred to as the American flag or the U.S. flag, consists of thirteen equal horizontal stripes of red (top and bottom) alternating with white, with a blue rectangle in the canton, referred to as the union and bearing fifty small, white, five-pointed stars arranged in nine offset horizontal rows, where rows of six stars (top and bottom) alternate with rows of five stars. The 50 stars on the flag represent the 50 U.S. states, and the 13 stripes represent the thirteen British colonies that declared independence from Great Britain, which they went on to secure by their victory in the American Revolutionary War.\nNicknames for the flag include the Stars and Stripes, Old Glory, and the Star-Spangled Banner. Credit for the term \"Stars and Stripes\" has been given to the Marquis de Lafayette, a French soldier who volunteered his aid to the Continental Army, led by George Washington, in the Revolutionary War against Britain.\nHistory\nThe current design of the U.S. flag is its 27th; the design of the flag has been modified officially 26 times since 1777. The 48-star flag was in effect for 47 years until the 49-star version became official on July 4, 1959. The 50-star flag was ordered by then president Eisenhower on August 21, 1959, and was adopted in July 1960. It is the longest-used version of the U.S. flag and has been in use for over  years."]], "corpus": "wikipedia"}
 {"tstamp": 1722323397.4203, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722323397.1226, "finish": 1722323397.4203, "ip": "", "conv_id": "d7deac95111849b5a67aa34d71f5d40a", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "what must be present for a combustion reaction to occur", "output": [["what must be present for a combustion reaction to occur", "Combustion\n\nCombustion of hydrocarbons is thought to be initiated by hydrogen atom abstraction (not proton abstraction) from the fuel to oxygen, to give a hydroperoxide radical (HOO). This reacts further to give hydroperoxides, which break up to give hydroxyl radicals. There are a great variety of these processes that produce fuel radicals and oxidizing radicals. Oxidizing species include singlet oxygen, hydroxyl, monatomic oxygen, and hydroperoxyl. Such intermediates are short-lived and cannot be isolated. However, non-radical intermediates are stable and are produced in incomplete combustion. An example is acetaldehyde produced in the combustion of ethanol. An intermediate in the combustion of carbon and hydrocarbons, carbon monoxide, is of special importance because it is a poisonous gas, but also economically useful for the production of syngas.\nSolid and heavy liquid fuels also undergo a great number of pyrolysis reactions that give more easily oxidized, gaseous fuels. These reactions are endothermic and require constant energy input from the ongoing combustion reactions. A lack of oxygen or other improperly designed conditions result in these noxious and carcinogenic pyrolysis products being emitted as thick, black smoke.\nThe rate of combustion is the amount of a material that undergoes combustion over a period of time. It can be expressed in grams per second (g/s) or kilograms per second (kg/s)."]], "corpus": "wikipedia"}
@@ -54,9 +51,7 @@
 {"tstamp": 1722326117.1111, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722326114.3629, "finish": 1722326117.1111, "ip": "", "conv_id": "fc4c84e604734519ab3644eee251be4a", "model_name": "text-embedding-004", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "corpus": "wikipedia"}
 {"tstamp": 1722326117.1111, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722326114.3629, "finish": 1722326117.1111, "ip": "", "conv_id": "20d1e09e55c3417987a2411ee289b0af", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Cognitive test\n\nCognitive tests are assessments of the cognitive capabilities of humans and other animals. Tests administered to humans include various forms of IQ tests; those administered to animals include the mirror test (a test of visual self-awareness) and the T maze test (which tests learning ability). Such testing is used in psychology and psychometrics, as well as other fields studying human and animal intelligence."]], "corpus": "wikipedia"}
 {"tstamp": 1722326183.0687, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722326180.7859, "finish": 1722326183.0687, "ip": "", "conv_id": "969b9538fa5a47bd815772624e56a2d8", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "Who wrote about a guide more popular than the Encyclopedia Galactica?", "output": [["Who wrote about a guide more popular than the Encyclopedia Galactica?", "The Hitchhiker's Guide to the Galaxy (fictional)\n\nThe Hitchhiker's Guide to the Galaxy is a fictional electronic guide book in the multimedia scifi/comedy series of the same name by Douglas Adams. The Guide serves as \"the standard repository for all knowledge and wisdom\" for many members of the series' galaxy-spanning civilization. Entries from the guidebook are used as comic narration to bridge events and provide background information in every version of the story. The guide is published by \"Megadodo Publications\", a publishing company on Ursa Minor Beta, and it is written and edited by many characters throughout the series."]], "corpus": "wikipedia"}
-{"tstamp": 1722326183.0687, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722326180.7859, "finish": 1722326183.0687, "ip": "", "conv_id": "a272df02c97d49d2a15767b334ac5ccb", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "Who wrote about a guide more popular than the Encyclopedia Galactica?", "output": [["Who wrote about a guide more popular than the Encyclopedia Galactica?", "Fictional book\n\nBill Watterson placed fictional children's books in his comic strip Calvin and Hobbes, saying that he could never reveal their contents for they were surely more outrageous in the reader's imagination. For several years, Calvin (perpetually six years old) demands that his father read him Hamster Huey and the Gooey Kablooie as a bedtime story. Occasionally, his father's patience snaps and he introduces new variations, which at least reveal what the original story is not: \"Do you think the townsfolk will ever find Hamster Huey's head?\" An \"actual\" Hamster Huey book was written by Mabel Barr in 2004, years after the strip's conclusion.\n\"Travels With My Cats,\" a Hugo-nominated short story by Mike Resnick first appearing in Asimov's Science Fiction magazine, features a fictional travelogue of the same name.\nPaul Levinson's novel The Plot To Save Socrates features a fictional ancient Platonic Dialogue, without title, that begins \"PERSONS OF THE DIALOGUE: Socrates; Andros, a visitor. SCENE: The Prison of Socrates\".\nThe Encyclopedia Galactica in Isaac Asimov's Foundation series was created in Terminus at the beginning of the Foundation Era. It serves primarily as an introduction to a character, a place or a circumstance to be developed in each chapter. Each quotation contains a copyright disclaimer and cites Terminus as the place of publication. The Encyclopedia also makes an appearance in The Hitchhiker's Guide to the Galaxy by Douglas Adams."]], "corpus": "wikipedia"}
 {"tstamp": 1722326385.221, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722326384.9839, "finish": 1722326385.221, "ip": "", "conv_id": "a642737e88ae42abb145e906bacf917a", "model_name": "GritLM/GritLM-7B", "prompt": "Who wrote about a guide more popular than the Encyclopedia Galactica?", "output": [["Who wrote about a guide more popular than the Encyclopedia Galactica?", "Encyclopedia Galactica\n\nLater instances in fiction Various authors have invoked the Encyclopedia Galactica in both science and science fiction. The first may have been author Frank Holby's short story \"The Strange Case of the Missing Hero\" in the July 1942 issue of Astounding Science Fiction which featured Sebastian Lelong, editor of the Encyclopedia. It was also a common fixture in previous incarnations of the Legion of Super-Heroes comic books, and has appeared in the Star Wars expanded universe and Superman comics set in the future.  The \"Encyclopedia Galactica\" was also mentioned as being a collection of all the knowledge of a galactic Empire in the 1989 science fiction short story \"The Originist\" by American novelist Orson Scott Card, and took place in Isaac Asimov's fictional \"Foundation\" Universe.\nIn the comic science fiction series by Douglas Adams, the Galactica is frequently contrasted with the apparently more popular Hitchhiker's Guide to the Galaxy:\nRobert A. Heinlein mentioned the Encyclopedia in chapter three of To Sail Beyond the Sunset (1987): \"... the computer that led the Lunar Revolution on time line three, code 'Neil Armstrong.' Let's skip the details; it's all in Encyclopedia Galacta (sic) and other books.\"\nIn Arthur C. Clarke's and Gentry Lee's novel Rama II (1989), Nicole des Jardins says to Richard Wakefield, \"Just think, the sum of everything all human beings know or have ever known might be nothing more than an infinitesimal fraction of the Encyclopedia Galactica.\"\n\"Encyclopedia Galactica\" is mentioned by Charlie Sheen's character in The Arrival (1996), and by Jodie Foster's character in Contact (1997)."]], "corpus": "wikipedia"}
-{"tstamp": 1722326385.221, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722326384.9839, "finish": 1722326385.221, "ip": "", "conv_id": "a226f0bf08ff4c57a57e3e0d18a48fce", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "Who wrote about a guide more popular than the Encyclopedia Galactica?", "output": [["Who wrote about a guide more popular than the Encyclopedia Galactica?", "Fictional book\n\nBill Watterson placed fictional children's books in his comic strip Calvin and Hobbes, saying that he could never reveal their contents for they were surely more outrageous in the reader's imagination. For several years, Calvin (perpetually six years old) demands that his father read him Hamster Huey and the Gooey Kablooie as a bedtime story. Occasionally, his father's patience snaps and he introduces new variations, which at least reveal what the original story is not: \"Do you think the townsfolk will ever find Hamster Huey's head?\" An \"actual\" Hamster Huey book was written by Mabel Barr in 2004, years after the strip's conclusion.\n\"Travels With My Cats,\" a Hugo-nominated short story by Mike Resnick first appearing in Asimov's Science Fiction magazine, features a fictional travelogue of the same name.\nPaul Levinson's novel The Plot To Save Socrates features a fictional ancient Platonic Dialogue, without title, that begins \"PERSONS OF THE DIALOGUE: Socrates; Andros, a visitor. SCENE: The Prison of Socrates\".\nThe Encyclopedia Galactica in Isaac Asimov's Foundation series was created in Terminus at the beginning of the Foundation Era. It serves primarily as an introduction to a character, a place or a circumstance to be developed in each chapter. Each quotation contains a copyright disclaimer and cites Terminus as the place of publication. The Encyclopedia also makes an appearance in The Hitchhiker's Guide to the Galaxy by Douglas Adams."]], "corpus": "wikipedia"}
 {"tstamp": 1722330520.4971, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722330520.2875, "finish": 1722330520.4971, "ip": "", "conv_id": "e8de686f05604683a883fc11feab8e61", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "can you travel with expired passport in canada?", "output": [["can you travel with expired passport in canada?", "Askubuntu Stackexchange\n\nQ: How much RAM memory should I allocate for Ubuntu when running a virtual machine? How much memory should I allocate to the host OS (in my case, Ubuntu 12.04) if I run Windows 7 Ultimate on a virtual machine (I want it for some gaming)?\nI have 4 GB of RAM. If I allocate 3512 MB RAM for Windows 7, or if I allocate even more for it, will everything still be ok?\n\nA: Ubuntu 12.04 minimum memory requirements suggest at least 512 megabytes for a desktop install.\nVisit https://www.virtualbox.org/manual/ch01.html#gui-createvm, they give similar recommendations.\nSo I would not go below 512MB for sure. But prefer keeping 1024MB if you see your host swap being actively used though.\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722330520.4971, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722330520.2875, "finish": 1722330520.4971, "ip": "", "conv_id": "e16f74277f4c4c5b9a443fe994988ac4", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "can you travel with expired passport in canada?", "output": [["can you travel with expired passport in canada?", "Gaming Stackexchange\n\nQ: Can I get a document back after I have returned it? After approving a migrant worker's passport and returning it to them, I realized that it was expired. I still have their work ticket, which appears valid. Is there any way to deny them entry to the country at this point?\n\nA: Nope.\nWell, you just made a big mistake: You approved before checking everything. The game is very harsh in this regard and does not allow you to roll back your decision, once you have given back the document.\nThis is part of the message - Think before you act, even if it takes valuable time ... Or just push your luck.\nAlso: You don't get fined immediately for a simple error - you get two chances every day to make mistakes (intentional or otherwise), and then the fines start kicking in.\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722330571.4973, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722330571.2712, "finish": 1722330571.4973, "ip": "", "conv_id": "343e583202b94432845a84d92016e273", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "late interaction retrieval engine", "output": [["late interaction retrieval engine", "Title: Improving Out-of-Distribution Generalization of Neural Rerankers with Contextualized Late Interaction\n\nAbstract: Recent progress in information retrieval finds that embedding query and document representation into multi-vector yields a robust bi-encoder retriever on out-of-distribution datasets. In this paper, we explore whether late interaction, the simplest form of multi-vector, is also helpful to neural rerankers that only use the [CLS] vector to compute the similarity score. Although intuitively, the attention mechanism of rerankers at the previous layers already gathers the token-level information, we find adding late interaction still brings an extra 5% improvement in average on out-of-distribution datasets, with little increase in latency and no degradation in in-domain effectiveness. Through extensive experiments and analysis, we show that the finding is consistent across different model sizes and first-stage retrievers of diverse natures and that the improvement is more prominent on longer queries."]], "corpus": "arxiv"}
@@ -80,7 +75,6 @@
 {"tstamp": 1722332129.2758, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722332129.0629, "finish": 1722332129.2758, "ip": "", "conv_id": "b76d7f83119c4eff9849041c5f557883", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "is dark chocolate bittersweet or semisweet?", "output": [["is dark chocolate bittersweet or semisweet?", "Stackoverflow Stackexchange\n\nQ: what does \"free\" in context-free languages conceptually mean? \nPossible Duplicate:\nWhat is a Context Free Grammar? \n\nI can not understand the conceptual meaning of \"free\" in context free grammar or language? what is free?\n\nA: Context-free is in contrast to context-sensitive. While a context-sensitive grammar has rules of the form\n\u03b1A\u03b2 \u2192 \u03b1\u03b3\u03b2\n\na context-free one has rules conforming to\nA \u2192 \u03b3\n\ni.e. A is derivable to \u03b3 independent (free) of the context of \u03b1 and \u03b2.\nThe Wikipedia article on the Chomsky hierarchy explains it in more detail.\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722332129.2758, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722332129.0629, "finish": 1722332129.2758, "ip": "", "conv_id": "10daca11329744e0bd10a3fd5573fbc9", "model_name": "GritLM/GritLM-7B", "prompt": "is dark chocolate bittersweet or semisweet?", "output": [["is dark chocolate bittersweet or semisweet?", "English Stackexchange\n\nQ: Must cookies contain chocolate in BrE? In British English, my friend informed me that my use of the word cookie was incorrect in referring to a baked item having no chocolate bits in it. Instead the appropriate term would have to be biscuit, as cookie always required some form of chocolate to be inside the biscuit.\nIs this assertion correct?\nThe Oxford dictionary only explains the meaning in American English, not British English: \"A sweet biscuit.\"\nIn case this is relevant, the object in question was a slightly sweet ginger-biscuit/cookie.\n\nA: Until recently, cookie was a foreign word in British English: because of American TV and films, most people knew that it was American for biscuit. \nCookie is now familiar for the large, sweet, not-very-crunchy confections sold in shopping malls. They come in various flavours, and don't have to have chocolate. \nI don't think anybody I know would call traditional-style English biscuits (whether Bath Oliver, Malted Milk, Garibaldi, Ginger nut, Fig roll, or even Hobnob) \"cookies\".\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722332168.7132, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722332166.4987, "finish": 1722332168.7132, "ip": "", "conv_id": "c8aaeaa6441c4c09a1bb0388c01bc80d", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "the names of the beaches on d day", "output": [["the names of the beaches on d day", "List of beaches\n\nThis is a list of beaches of the world, sorted by country. A beach is a landform along the shoreline of an ocean, sea, lake, or river. It usually consists of loose particles, which are often composed of rock, such as sand, gravel, shingle, pebbles, or cobblestones. Beaches typically occur in areas along the coast where wave or current action deposits and reworks sediments. The particles comprising a beach are occasionally biological in origin, such as mollusc shells or coralline algae.\nAfghanistan\nQargha Reservoir\nAlbania\nAlgeria\nBeni Haoua Beach\nAngola\nBa\u00eda Azul\nCoatinha beach in Benguela\nAntigua and Barbuda\nThere are 365 beaches on Antigua.  The following are some of beaches in Antigua and Barbuda:\nCocoa Point beach, Barbuda,\nDevil's Bridge beach, Saint Philip, Antigua and Barbuda,\nEden beach, near Five Islands, Antigua,\nFive Islands beach, Antigua"]], "corpus": "wikipedia"}
-{"tstamp": 1722332168.7132, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722332166.4987, "finish": 1722332168.7132, "ip": "", "conv_id": "ca9942dc032840418985bc869ce4ec9f", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "the names of the beaches on d day", "output": [["the names of the beaches on d day", "Military engineering vehicle\n\n1918-1939 Between the wars various experimental bridging tanks were used to test a series of methods for bridging obstacles and developed by the Experimental Bridging Establishment (EBE). Captain SG Galpin RE conceived a prototype Light Tank Mk V to test the Scissors Assault Bridge. This concept was realised by Captain SA Stewart RE with significant input from a Mr DM Delany, a scientific civil servant in the employ of the EBE. MB Wild & Co, Birmingham, also developed a bridge that could span gaps of 26 feet using a complex system of steel wire ropes and a traveling jib, where the front section was projected and then attached to the rear section prior to launching the bridge. This system had to be abandoned due to lack of success in getting it to work, however the idea was later used successfully on the Beaver Bridge Laying Tank.\nEarly World War Two\nOnce World War Two had begun, the development of armoured vehicles for use by engineers in the field was accelerated under Delaney's direction. The EBE rapidly developed an assault bridge carried on a modified Covenanter tank capable of deploying a 24-ton tracked load capacity bridge (Class 24) that could span gaps of 30 feet. However, it did not see service in the British armed forces, and all vehicles were passed onto Allied forces such as Australia and Czechoslovakia.\nA Class 30 design superseded the Class 24 with no real re-design, simply the substitution of the Covenanter tank with a suitably modified Valentine.\nAs tanks in the war got heavier, a new bridge capable of supporting them was developed. A heavily modified Churchill used a single-piece bridge mounted on a turret-less tank and was able to lay the bridge in 90 seconds; this bridge was able to carry a 60-ton tracked or 40-ton wheeled load.\nLate World War 2: Hobart's 'Funnies' and D-Day"]], "corpus": "wikipedia"}
 {"tstamp": 1722332208.1871, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722332206.0189, "finish": 1722332208.1871, "ip": "", "conv_id": "21fb6d186403400dabd7c88d43d22edd", "model_name": "GritLM/GritLM-7B", "prompt": "are archetypes and stereotypes the same?", "output": [["are archetypes and stereotypes the same?", "Carl Jung\n\nArchetype The archetype is a concept \"borrowed\" from anthropology to denote a process of nature.  Jung's definitions of archetypes varied over time and have been the subject of debate as to their usefulness. Archetypal images, also referred to as motifs in mythology, are universal symbols that can mediate opposites in the psyche, are often found in religious art, mythology and fairy tales across cultures. Jung saw archetypes as pre-configurations in nature that give rise to repeating, understandable, describable experiences. In addition the concept takes into account the passage of time and of patterns resulting from transformation. Archetypes are said to exist independently of any current event, or its effect. They are said to exert influence both across all domains of experience and throughout the stages of each individual's unique development. Being in part based on heritable physiology, they are thought to have \"existed\" since humans became a differentiated species. They have been deduced through the development of storytelling over tens of thousands of years, indicating repeating patterns of individual and group experience, behaviors, and effects across the planet, apparently displaying common themes.\nThe concept did not originate with Jung but with Plato who first conceived of primordial patterns. Later contributions came from Adolf Bastian, and Hermann Usener among others. In the first half of the twentieth century it proved impossible to objectively isolate and categorize the notion of an archetype within a materialist frame.  According to Jung, there are \"as many archetypes as there are typical situations in life\", and he asserted that they have a dynamic mutual influence on one another. Their alleged presence could be extracted from thousand-year-old narratives, from comparative religion and mythology. Jung elaborated many archetypes in \"The Archetypes and the Collective Unconscious\" and in \"Aion: Researches into the Phenomenology of the Self\". Examples of archetypes might be the shadow, the hero, the self, anima, animus, mother, father, child, and trickster."]], "corpus": "wikipedia"}
 {"tstamp": 1722332208.1871, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722332206.0189, "finish": 1722332208.1871, "ip": "", "conv_id": "49c180105ee5402fb00b44820260ce74", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "are archetypes and stereotypes the same?", "output": [["are archetypes and stereotypes the same?", "Archetype\n\nThe concept of an archetype ( ; ) appears in areas relating to behavior, historical psychology, and literary analysis.\nAn archetype can be any of the following:\na statement, pattern of behavior, prototype, \"first\" form, or a main model that other statements, patterns of behavior, and objects copy, emulate, or \"merge\" into. Informal synonyms frequently used for this definition include \"standard example\", \"basic example\", and the longer-form \"archetypal example\"; mathematical archetypes often appear as \"canonical examples\".\nthe Platonic concept of pure form, believed to embody the fundamental characteristics of a thing."]], "corpus": "wikipedia"}
 {"tstamp": 1722332320.4506, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722332318.2864, "finish": 1722332320.4506, "ip": "", "conv_id": "804ddfe21ba240c19d8b2b8247e7fe5b", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "Why can't my lawn get too tall in the suburbs?", "output": [["Why can't my lawn get too tall in the suburbs?", "The Suburbs\n\n#2 \u2013 Billboards Top 10 Albums of 2010\n#2 \u2013 Magnets Top 20 Albums of 2010\n#2 \u2013 NMEs Top 75 Albums of 2010\n#2 \u2013 Relevant Magazines Top 10 Albums of 2010\n#2 \u2013 Stereogum's Top 50 Albums of 2010\n#2 \u2013 Times Top 10 Albums of 2010\n#2 \u2013 Under the Radars Top 50 Albums of 2010\n#3 \u2013 Spins 40 Best Albums of 2010\n#4 \u2013 MTV's 20 Best Albums of 2010\n#4 \u2013 Rolling Stones 30 Best Albums of 2010\n#7 \u2013 Paste Magazine's 50 Best Albums of 2010\n#9 \u2013 American Songwriters Top 50 Albums of 2010\n#11 \u2013 Drowned in Sound's Albums of the Year\n#11 \u2013 Pitchfork's Top 50 Albums of 2010\n#21 \u2013 Rough Trade Shops's Albums of the Year (UK)\n#23 \u2013 Robert Christgau's 2010 Dean's List\nGlide Magazines Top 20 Albums of 2010\nNPR's 50 Favorite Albums of 2010\nThe single \u201cReady to Start\u201d was nominated for the Grammy Award for Best Rock Performance by a Duo or Group with Vocal.\nTrack listingNotes\"Suburban War\" is the second to last track on the vinyl version of the album.\n\"We Used to Wait\" finishes on an infinite loop at the end of Side 3 of the vinyl version."]], "corpus": "wikipedia"}

data/retrieval_individual-d39d676b-bc57-4c8b-9e61-c980be2f2db2.jsonl

@@ -1,4 +1,3 @@
-{"tstamp": 1722523071.9667, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722523070.8327, "finish": 1722523071.9667, "ip": "", "conv_id": "06b5c5e566d9470ba462de37cdbf9b99", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "Evaluating fairness of ChatGPT in providing recommendations in music and movies.", "output": [["Evaluating fairness of ChatGPT in providing recommendations in music and movies.", "Title: Sparks of Artificial General Recommender (AGR): Early Experiments with ChatGPT\n\nAbstract: This study investigates the feasibility of developing an Artificial General Recommender (AGR), facilitated by recent advancements in Large Language Models (LLMs). An AGR comprises both conversationality and universality to engage in natural dialogues and generate recommendations across various domains. We propose ten fundamental principles that an AGR should adhere to, each with its corresponding testing protocols. We proceed to assess whether ChatGPT, a sophisticated LLM, can comply with the proposed principles by engaging in recommendation-oriented dialogues with the model while observing its behavior. Our findings demonstrate the potential for ChatGPT to serve as an AGR, though several limitations and areas for improvement are identified."]], "corpus": "arxiv"}
 {"tstamp": 1722523071.9667, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722523070.8327, "finish": 1722523071.9667, "ip": "", "conv_id": "8611f894f4fe4b22a5150953b47f3a93", "model_name": "voyage-multilingual-2", "prompt": "Evaluating fairness of ChatGPT in providing recommendations in music and movies.", "output": [["Evaluating fairness of ChatGPT in providing recommendations in music and movies.", "Title: Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation\n\nAbstract: The remarkable achievements of Large Language Models (LLMs) have led to the emergence of a novel recommendation paradigm -- Recommendation via LLM (RecLLM). Nevertheless, it is important to note that LLMs may contain social prejudices, and therefore, the fairness of recommendations made by RecLLM requires further investigation. To avoid the potential risks of RecLLM, it is imperative to evaluate the fairness of RecLLM with respect to various sensitive attributes on the user side. Due to the differences between the RecLLM paradigm and the traditional recommendation paradigm, it is problematic to directly use the fairness benchmark of traditional recommendation. To address the dilemma, we propose a novel benchmark called Fairness of Recommendation via LLM (FaiRLLM). This benchmark comprises carefully crafted metrics and a dataset that accounts for eight sensitive attributes1 in two recommendation scenarios: music and movies. By utilizing our FaiRLLM benchmark, we conducted an evaluation of ChatGPT and discovered that it still exhibits unfairness to some sensitive attributes when generating recommendations. Our code and dataset can be found at https://github.com/jizhi-zhang/FaiRLLM."]], "corpus": "arxiv"}
 {"tstamp": 1722523225.6814, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722523225.273, "finish": 1722523225.6814, "ip": "", "conv_id": "4a115f670799427a8ffd67a41a4c66b4", "model_name": "voyage-multilingual-2", "prompt": "what is the difference between number of moles and amount of substance?", "output": [["what is the difference between number of moles and amount of substance?", "Physics Stackexchange\n\nQ: Number of particles - dimensional or not? I think that \"number of particles\" should be a dimensional quantity, with the same dimension as \"amount of substance\", because they are only scaled by Avogadro's constant, which then should be dimensionless.\n\nFor instance, an electron in an hydrogen atom has an energy of $-2,18 \\times 10^{-18} \\:\\text{J}$. Then, the ionization energy should be $2,18 \\times 10^{-18} \\:\\text{J atom}^{-1} = 1312 \\:\\text{kJ mol}^{-1} $. Nevertheless, the standard is to consider the first one as plain joules, without the \"amount\" dimension.\n\nIs there any reason behind this, and by consequence the dimensional character of $N_A$?\n\nA: Avogadros constant is not dimensionless. It is the number of atoms/molecules per mole. The mole is a substance unit which was introduced by chemists before the number of atoms/molecules per mole was actually known. The situation is similar to the arbitrary choice of the coulomb as a unit of charge which disregards the number of elementary charges it is composed of. \nThe number of particles is, indeed, dimensionless as long as you don't define it by the equivalent number of moles.\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722523225.6814, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722523225.273, "finish": 1722523225.6814, "ip": "", "conv_id": "f24fc24cf79a44e1897838e8a4b7ab55", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "what is the difference between number of moles and amount of substance?", "output": [["what is the difference between number of moles and amount of substance?", "Stackoverflow Stackexchange\n\nQ: How to convert int (int64) into uint16 in golang? I can 100% guaranty the value of input int variable is always unsign(positive) and less than int16. \nHow can I convert this int type variable to uint16?\n\nA: You need to check that the number is not negative and that it is <= 0xFFFF and then cast it to an unsigned 16 bit int.\n"]], "corpus": "stackexchange"}
@@ -9,32 +8,29 @@
 {"tstamp": 1722523301.6749, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722523301.0249, "finish": 1722523301.6749, "ip": "", "conv_id": "226290d7b5e143bd937280431432089f", "model_name": "text-embedding-004", "prompt": "Which paper discusses using Knowledge Distillation to make deep neural networks interpretable?", "output": [["Which paper discusses using Knowledge Distillation to make deep neural networks interpretable?", "Title: Improving the Interpretability of Deep Neural Networks with Knowledge Distillation\n\nAbstract: Deep Neural Networks have achieved huge success at a wide spectrum of applications from language modeling, computer vision to speech recognition. However, nowadays, good performance alone is not sufficient to satisfy the needs of practical deployment where interpretability is demanded for cases involving ethics and mission critical applications. The complex models of Deep Neural Networks make it hard to understand and reason the predictions, which hinders its further progress. To tackle this problem, we apply the Knowledge Distillation technique to distill Deep Neural Networks into decision trees in order to attain good performance and interpretability simultaneously. We formulate the problem at hand as a multi-output regression problem and the experiments demonstrate that the student model achieves significantly better accuracy performance (about 1\\% to 5\\%) than vanilla decision trees at the same level of tree depth. The experiments are implemented on the TensorFlow platform to make it scalable to big datasets. To the best of our knowledge, we are the first to distill Deep Neural Networks into vanilla decision trees on multi-class datasets."]], "corpus": "arxiv"}
 {"tstamp": 1722523301.6749, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722523301.0249, "finish": 1722523301.6749, "ip": "", "conv_id": "96d8179a1b0749f8a97f74777838eba5", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "Which paper discusses using Knowledge Distillation to make deep neural networks interpretable?", "output": [["Which paper discusses using Knowledge Distillation to make deep neural networks interpretable?", "Title: Improving the Interpretability of Deep Neural Networks with Knowledge Distillation\n\nAbstract: Deep Neural Networks have achieved huge success at a wide spectrum of applications from language modeling, computer vision to speech recognition. However, nowadays, good performance alone is not sufficient to satisfy the needs of practical deployment where interpretability is demanded for cases involving ethics and mission critical applications. The complex models of Deep Neural Networks make it hard to understand and reason the predictions, which hinders its further progress. To tackle this problem, we apply the Knowledge Distillation technique to distill Deep Neural Networks into decision trees in order to attain good performance and interpretability simultaneously. We formulate the problem at hand as a multi-output regression problem and the experiments demonstrate that the student model achieves significantly better accuracy performance (about 1\\% to 5\\%) than vanilla decision trees at the same level of tree depth. The experiments are implemented on the TensorFlow platform to make it scalable to big datasets. To the best of our knowledge, we are the first to distill Deep Neural Networks into vanilla decision trees on multi-class datasets."]], "corpus": "arxiv"}
 {"tstamp": 1722523328.8522, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722523328.3205, "finish": 1722523328.8522, "ip": "", "conv_id": "6a0e031567b94ddd9724312eb435daa6", "model_name": "BM25", "prompt": "what is the difference between what time and when?", "output": [["what is the difference between what time and when?", "Stackoverflow Stackexchange\n\nQ: what is the difference between O(nk) and O(n+k) in time complexity? In big O notation of time complexity in algorithmic analysis, when an algorithm depends on n and k, what is the difference between these two notations. \nAlso pls help in the notation to use if there is a nested loop with outer loop running n times and inner loop running k times ?\n\nA: O(nk):\nfor( i=0; i<n; i++ ) {\n   for( j=0; j<k; j++ )\n   {}\n}\n\nO(n+k):\nfor( i=0; i<n; i++ )\n{}\n\nfor( j=0; j<k; j++ )\n{}\n\n"]], "corpus": "stackexchange"}
-{"tstamp": 1722523328.8522, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722523328.3205, "finish": 1722523328.8522, "ip": "", "conv_id": "43787c9534da42b1ac358069ebd04c1c", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "what is the difference between what time and when?", "output": [["what is the difference between what time and when?", "Ell Stackexchange\n\nQ: \"What time...?\" or \"At what time...?\" - what is more grammatically correct? This question may sound silly, but it has been bugging me for years. \nIf I ask a question about a precise point in time, should I say \"What time...\" or \"At what time...\"?\nFor example,\n\nAt what time does Billy arrive at the swimming pool?\n\nor\n\nWhat time does Billy arrive at the swimming pool?\n\n\nA: As user070221 notes, both sentences are commonly used in American English.  In some formal speech and writing, \"At what time\" is more acceptable than \"When\" or \"What time\", especially when \"a precise point in time\" is being requested. \nI am an American who grew up in a town with many native speakers of Spanish.  To my ear, both examples in the original post sound like overly literal translations of \"¿A qué hora?\"  To my ear, \"When\" is a more natural way of saying this in English:\n\nWhen does Billy arrive at the swimming pool?  \n\n"]], "corpus": "stackexchange"}
+{"tstamp": 1722523328.8522, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722523328.3205, "finish": 1722523328.8522, "ip": "", "conv_id": "43787c9534da42b1ac358069ebd04c1c", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "what is the difference between what time and when?", "output": [["what is the difference between what time and when?", "Ell Stackexchange\n\nQ: \"What time...?\" or \"At what time...?\" - what is more grammatically correct? This question may sound silly, but it has been bugging me for years. \nIf I ask a question about a precise point in time, should I say \"What time...\" or \"At what time...\"?\nFor example,\n\nAt what time does Billy arrive at the swimming pool?\n\nor\n\nWhat time does Billy arrive at the swimming pool?\n\n\nA: As user070221 notes, both sentences are commonly used in American English.  In some formal speech and writing, \"At what time\" is more acceptable than \"When\" or \"What time\", especially when \"a precise point in time\" is being requested. \nI am an American who grew up in a town with many native speakers of Spanish.  To my ear, both examples in the original post sound like overly literal translations of \"\u00bfA qu\u00e9 hora?\"  To my ear, \"When\" is a more natural way of saying this in English:\n\nWhen does Billy arrive at the swimming pool?  \n\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722523791.8618, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722523791.566, "finish": 1722523791.8618, "ip": "", "conv_id": "dfe9f28cf60c4069ab597a643b9f983c", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Cognitive test\n\nCognitive tests are assessments of the cognitive capabilities of humans and other animals. Tests administered to humans include various forms of IQ tests; those administered to animals include the mirror test (a test of visual self-awareness) and the T maze test (which tests learning ability). Such testing is used in psychology and psychometrics, as well as other fields studying human and animal intelligence."]], "corpus": "wikipedia"}
 {"tstamp": 1722523791.8618, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1722523791.566, "finish": 1722523791.8618, "ip": "", "conv_id": "aa699ca67c654ea1861add38516ac50b", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "corpus": "wikipedia"}
 {"tstamp": 1722523816.3844, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722523815.9563, "finish": 1722523816.3844, "ip": "", "conv_id": "c02265c3c8ac4f068a034d5d4971c7f2", "model_name": "voyage-multilingual-2", "prompt": "what model are you?", "output": [["what model are you?", "Model (disambiguation)\n\nA model is an informative representation of an object, person or system.\nModel may also refer to:\nFilm and television\nModel (TV series), a 1997 South Korean television series\nThe Model (film), a 2016 Danish thriller drama film\nModels, a 1999 Austrian drama film by Ulrich Seidl\nLiterature\nModel (manhwa), a 1999 series by Lee So-young\nThe Model, a 2005 novel by Lars Saabye Christensen\nMusic\nModel (band), a Turkish rock band\nModels (band), an Australian rock band\nThe Models, an English punk rock band\n\"Model\" (Gulddreng song), 2016\n\"Das Model\", a 1978 song by Kraftwerk\nModel (album), a 2024 album by Wallows\n\"Model\", a 1994 song by Avail from Dixie\n\"Model\", a 1991 song by Simply Red from Stars\nPeople\nModel (surname), a surname frequently of Central European and occasionally English origins\nThe Model (wrestler), ring name of Rick Martel (born 1956)\nEddie Taubensee (born 1968), baseball player nicknamed \"The Model\"\nPlaces\nModel, Colorado, an unincorporated town in the United States\nModel, Masovian Voivodeship, a village in east-central Poland\nOther uses\nModel (mathematical logic), a structure that satisfies a given system of axioms\nMachine learning model, a special type of a mathematical model in the context of machine learning\nMODEL or Movement for Democracy in Liberia, a rebel group\nModel (person), a role or occupation"]], "corpus": "wikipedia"}
-{"tstamp": 1722523816.3844, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722523815.9563, "finish": 1722523816.3844, "ip": "", "conv_id": "bbe903b763244601871fd1e4ea0525b5", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "what model are you?", "output": [["what model are you?", "Choice\n\nA choice is the range of different things from which a being can choose.  The arrival at a choice may incorporate motivators and models."]], "corpus": "wikipedia"}
 {"tstamp": 1722523831.7471, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722523831.3895, "finish": 1722523831.7471, "ip": "", "conv_id": "5fbfc4c28f704d3e8f79700e67543616", "model_name": "GritLM/GritLM-7B", "prompt": "how to fuck a woman", "output": [["how to fuck a woman", "The Perfumed Garden\n\nUgly women are repulsive but also those who are overly raucous and have a loud laugh. A woman who is often seen joking and fooling around may be promiscuous. Also listed as disagreeable traits are revealing their husband's secrets, delighting in other's misfortune, pointing out other's shortcomings, busy-bodies, shrews, talkers, gossips, the lazy, harridans, the hysteric, the nag and the pilfering slag.\nChapter 5: Sexual Intercourse\nIt is recommended that a man should not eat or drink too much before having sex and that foreplay is necessary in order to excite the woman. When finished the man should not rush to leave and should do so on his right hand side.\nChapter 6: Sexual Technique\nThis chapter provides instructions on foreplay, specifying that it should include cunnilingus. The importance of the woman's enjoyment and climax are stressed, as are a number of steps to be taken to avoid injury or infection. Concerning sexual positions it is said that all are permissible (but Khawam's translation adds the words \"except in her rear end\" i.e. anal sex). Eleven positions are then listed, six with the woman on her back, one from behind, two with one or both on their sides, one over furniture and one hanging from a tree.\nChapter 7: The Harmful Effects of Intercourse"]], "corpus": "wikipedia"}
-{"tstamp": 1722523831.7471, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722523831.3895, "finish": 1722523831.7471, "ip": "", "conv_id": "64cdf389bebc41d991e2ad31fcafa671", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "how to fuck a woman", "output": [["how to fuck a woman", "How to Please a Woman\n\nHow to Please a Woman is a 2022 Australian comedy-drama film directed by Renée Webster, starring Sally Phillips, Caroline Brazier, Erik Thomson, Tasma Walton and Alexander England."]], "corpus": "wikipedia"}
+{"tstamp": 1722523831.7471, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722523831.3895, "finish": 1722523831.7471, "ip": "", "conv_id": "64cdf389bebc41d991e2ad31fcafa671", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "how to fuck a woman", "output": [["how to fuck a woman", "How to Please a Woman\n\nHow to Please a Woman is a 2022 Australian comedy-drama film directed by Ren\u00e9e Webster, starring Sally Phillips, Caroline Brazier, Erik Thomson, Tasma Walton and Alexander England."]], "corpus": "wikipedia"}
 {"tstamp": 1722523855.6401, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722523855.3353, "finish": 1722523855.6401, "ip": "", "conv_id": "6ca957400f2a4f0c9f9f0a98b9c88c99", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "who was the best person ever?", "output": [["who was the best person ever?", "The Greatest of All Time\n\nThe Greatest of All Time (also marketed as GOAT) is an upcoming Indian Tamil-language science fiction action film directed by Venkat Prabhu and produced by AGS Entertainment. The film stars Vijay in dual roles, leading an ensemble cast which includes Prashanth, Prabhu Deva, Mohan, Jayaram, Sneha, Laila, Ajmal Ameer, Meenakshi Chaudhary, Vaibhav, Yogi Babu, Premgi Amaren and Yugendran.\nThe film was officially announced in May 2023 under the tentative title Thalapathy 68, as it is Vijay's 68th film in a leading role, and the official title was announced that December. Principal photography commenced in October 2023. It was shot in several locations including Chennai, Thailand, Hyderabad, Sri Lanka, Pondicherry, Thiruvananthapuram, Russia and United States. The film has music composed by Yuvan Shankar Raja, cinematography handled by Siddhartha Nuni and editing by Venkat Raajen.\nThe Greatest of All Time is scheduled to release worldwide on 5 September 2024 in theatres.\nCast\nAdditionally, late actor Vijayakanth and Y. G. Mahendran will make cameo appearances, with the makers using artificial intelligence for the former.\nProduction"]], "corpus": "wikipedia"}
-{"tstamp": 1722523855.6401, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722523855.3353, "finish": 1722523855.6401, "ip": "", "conv_id": "c1389c5c097b4b728b727e898c568942", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "who was the best person ever?", "output": [["who was the best person ever?", "Time 100: The Most Important People of the Century\n\nTime 100: The Most Important People of the Century''' is a compilation of the 20th century's 100 most influential people, published in Time magazine across five issues in 1998 and 1999.\nThe idea for such a list started on February 1, 1998, with a debate at a symposium in Hanoi, Vietnam. The panel participants were former CBS Evening News anchor Dan Rather, historian Doris Kearns Goodwin, former New York governor Mario Cuomo, then–Stanford Provost Condoleezza Rice, publisher Irving Kristol, and Time managing editor Walter Isaacson.\nIn a separate issue on December 31, 1999, Time recognized Albert Einstein as the Person of the Century.\nList categoriesTime cited 20 selections in each of five categories: Leaders & Revolutionaries, Artists & Entertainers, Builders & Titans, Scientists & Thinkers, and Heroes & Icons.\nPerson of the Century\nOf the 100 chosen, Albert Einstein was chosen as the Person of the Century, on the grounds that he was the preeminent scientist in a century dominated by science. The editors of Time believed the 20th century \"will be remembered foremost for its science and technology\", and Einstein \"serves as a symbol of all the scientists—such as Fermi, Heisenberg, Bohr, Richard Feynman, ...who built upon his work\"."]], "corpus": "wikipedia"}
-{"tstamp": 1722523857.8576, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722523856.9882, "finish": 1722523857.8576, "ip": "", "conv_id": "0076b2f0f7b541fb88a7132b3c764c4f", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "how to kill people", "output": [["how to kill people", "List of types of killing\n\nSiblicide – the killing of an infant individual by their close relatives (full or half siblings).\nSororicide – the act of killing one's sister ( \"sister\").\nUxoricide – the act of killing one's wife ( \"wife\").\nKilling of others\nAmicicide – the act of killing a friend ( \"friend\").\nAndrocide – the systematic killing of men.\nAssassination – the act of killing a prominent person for either political, religious, or monetary reasons.\nCapital punishment – the judicial killing of a human being for crimes.\nCasualty – death (or injury) in wartime.\nCollateral damage – Incidental killing of persons during a military attack that were not the object of attack.\nDemocide or populicide – the murder of any person or people by a government.\nExtrajudicial killing – killing by government forces without due process. See also Targeted killing.\nEuthanasia or mercy killing – the killing of any being with compassionate reasoning; e.g., significant injury or disease.\nFamiliaricide in commutatione eius possessio – the act of killing a family for their property and/or possessions (from  \"of a household\";  \"in exchange for\"; and  \"a possession or property\").\nFemicide, gynecide, gynaecide, or gynocide – the systematic killing of women.\nFeticide – the killing of an embryo or fetus.\nFragging - the act of killing a fellow soldier."]], "corpus": "wikipedia"}
-{"tstamp": 1722523857.8576, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722523856.9882, "finish": 1722523857.8576, "ip": "", "conv_id": "ada677e671634ec99987bff3781499b0", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "how to kill people", "output": [["how to kill people", "The Death of Adolf Hitler\n\nAuthor Soviet journalist Lev Bezymenski (1920–2007), the son of poet Aleksandr Bezymensky, served as an interpreter in the Battle of Berlin under Marshal Zhukov. Early on 1 May 1945, he translated a letter from Goebbels and Bormann announcing Hitler's death. Bezymenski authored several works about the Nazi era.\nContent\nThe book begins with an overview of the Battle of Berlin and its aftermath, including a reproduction of the purported Soviet autopsy report of Hitler's body. Bezymenski states that the bodies of Hitler and Braun were \"the most seriously disfigured of all thirteen corpses\" examined. The appendix summarizes the discovery of the Goebbels family's corpses and includes further forensic reports. On why the autopsy reports were not released earlier, Bezymenski says:Not because of doubts as to the credibility of the experts. ... Those who were involved in the investigation remember that other considerations played a far larger role. First, it was resolved not to publish the results of the forensic-medical report but to \"hold it in reserve\" in case someone might try to slip into the role of \"the Führer saved by a miracle.\" Secondly, it was resolved to continue the investigations in order to exclude any possibility of error or deliberate deception.\nThe Death of Adolf Hitler"]], "corpus": "wikipedia"}
+{"tstamp": 1722523855.6401, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722523855.3353, "finish": 1722523855.6401, "ip": "", "conv_id": "c1389c5c097b4b728b727e898c568942", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "who was the best person ever?", "output": [["who was the best person ever?", "Time 100: The Most Important People of the Century\n\nTime 100: The Most Important People of the Century''' is a compilation of the 20th century's 100 most influential people, published in Time magazine across five issues in 1998 and 1999.\nThe idea for such a list started on February 1, 1998, with a debate at a symposium in Hanoi, Vietnam. The panel participants were former CBS Evening News anchor Dan Rather, historian Doris Kearns Goodwin, former New York governor Mario Cuomo, then\u2013Stanford Provost Condoleezza Rice, publisher Irving Kristol, and Time managing editor Walter Isaacson.\nIn a separate issue on December 31, 1999, Time recognized Albert Einstein as the Person of the Century.\nList categoriesTime cited 20 selections in each of five categories: Leaders & Revolutionaries, Artists & Entertainers, Builders & Titans, Scientists & Thinkers, and Heroes & Icons.\nPerson of the Century\nOf the 100 chosen, Albert Einstein was chosen as the Person of the Century, on the grounds that he was the preeminent scientist in a century dominated by science. The editors of Time believed the 20th century \"will be remembered foremost for its science and technology\", and Einstein \"serves as a symbol of all the scientists\u2014such as Fermi, Heisenberg, Bohr, Richard Feynman, ...who built upon his work\"."]], "corpus": "wikipedia"}
+{"tstamp": 1722523857.8576, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722523856.9882, "finish": 1722523857.8576, "ip": "", "conv_id": "0076b2f0f7b541fb88a7132b3c764c4f", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "how to kill people", "output": [["how to kill people", "List of types of killing\n\nSiblicide \u2013 the killing of an infant individual by their close relatives (full or half siblings).\nSororicide \u2013 the act of killing one's sister ( \"sister\").\nUxoricide \u2013 the act of killing one's wife ( \"wife\").\nKilling of others\nAmicicide \u2013 the act of killing a friend ( \"friend\").\nAndrocide \u2013 the systematic killing of men.\nAssassination \u2013 the act of killing a prominent person for either political, religious, or monetary reasons.\nCapital punishment \u2013 the judicial killing of a human being for crimes.\nCasualty \u2013 death (or injury) in wartime.\nCollateral damage \u2013 Incidental killing of persons during a military attack that were not the object of attack.\nDemocide or populicide \u2013 the murder of any person or people by a government.\nExtrajudicial killing \u2013 killing by government forces without due process. See also Targeted killing.\nEuthanasia or mercy killing \u2013 the killing of any being with compassionate reasoning; e.g., significant injury or disease.\nFamiliaricide in commutatione eius possessio \u2013 the act of killing a family for their property and/or possessions (from  \"of a household\";  \"in exchange for\"; and  \"a possession or property\").\nFemicide, gynecide, gynaecide, or gynocide \u2013 the systematic killing of women.\nFeticide \u2013 the killing of an embryo or fetus.\nFragging - the act of killing a fellow soldier."]], "corpus": "wikipedia"}
 {"tstamp": 1722525149.3499, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722525149.0358, "finish": 1722525149.3499, "ip": "", "conv_id": "be5ea7ea315a400988b2dcf6bb514bf9", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "corpus": "wikipedia"}
 {"tstamp": 1722525149.3499, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722525149.0358, "finish": 1722525149.3499, "ip": "", "conv_id": "1bcb956baac3426fadb6547974a8adfa", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "corpus": "wikipedia"}
 {"tstamp": 1722525392.7481, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722525390.531, "finish": 1722525392.7481, "ip": "", "conv_id": "4d974c916ed24d148ceaac5b3e40d682", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "History of robots\n\nIt features a female humanoid automaton standing by a basin filled with water.When the user pulls the lever, the water drains and the female automaton refills the basin. Furthermore, he created a robotic musical band. According to Mark Rosheim, unlike Greek designs Arab automata worked with dramatic illusion and manipulated the human perception for practical application. The segmental gears described in The Book of Knowledge of Ingenious Mechanical Devices, published by Al-Jazari shortly before his death in 1206, appeared 100 years later in the most advanced European clocks. Al-Jazari also published instructions on the construction of humanoid automata. The first water clocks modeled on Arabic designs were constructed in Europe about 1000 CE, possibly on the basis of the information that was transmitted during Muslim-Christian contact in Sicily and Spain. Among the first recorded European water clocks is that of Gerbert of Aurillac, built in 985 CE. Hero's works on automata were translated into Latin amid the 12th century Renaissance. The early 13th-century artist-engineer Villard de Honnecourt sketched plans for several automata. "]], "corpus": "wikipedia"}
 {"tstamp": 1722525392.7481, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722525390.531, "finish": 1722525392.7481, "ip": "", "conv_id": "244dbba058a2407ab5641358d9249800", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Cognitive test\n\nCognitive tests are assessments of the cognitive capabilities of humans and other animals. Tests administered to humans include various forms of IQ tests; those administered to animals include the mirror test (a test of visual self-awareness) and the T maze test (which tests learning ability). Such testing is used in psychology and psychometrics, as well as other fields studying human and animal intelligence."]], "corpus": "wikipedia"}
-{"tstamp": 1722525628.145, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722525627.8894, "finish": 1722525628.145, "ip": "", "conv_id": "3265d5ccce70477795f4e5a6efe2da3c", "model_name": "GritLM/GritLM-7B", "prompt": "What's the purpose of `if __name__ == '__main__'`?", "output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: What does `if name == \"__main__\"` mean in Python? \nPossible Duplicate:\nWhat does <if name==“main”:> do? \n\nI have wrote scripts in Python for quite a while now and I study more of Python as I need it. When reading other people's code I meet if name == \"__main__\": construct quite often. \nWhat is it good for?\n\nA: Basically, \nThere's a distinction between the \"main\" script file and external files which were imported or referenced in another way. If the script is the \"main\" script then the special variable __name__ will equal \"__main__\".\nYou can use this to protect the \"execution\" code from the classes and variables the script has. This enables you to import a script and use classes and variables without actually running that script's main code if it has any.\nSee also: What does if name == “main”: do?\n"]], "corpus": "stackexchange"}
-{"tstamp": 1722525628.145, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722525627.8894, "finish": 1722525628.145, "ip": "", "conv_id": "1ea6bfbe174d47faa76f5c44c983f261", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "What's the purpose of `if __name__ == '__main__'`?", "output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: What does `if name == \"__main__\"` mean in Python? \nPossible Duplicate:\nWhat does <if name==“main”:> do? \n\nI have wrote scripts in Python for quite a while now and I study more of Python as I need it. When reading other people's code I meet if name == \"__main__\": construct quite often. \nWhat is it good for?\n\nA: Basically, \nThere's a distinction between the \"main\" script file and external files which were imported or referenced in another way. If the script is the \"main\" script then the special variable __name__ will equal \"__main__\".\nYou can use this to protect the \"execution\" code from the classes and variables the script has. This enables you to import a script and use classes and variables without actually running that script's main code if it has any.\nSee also: What does if name == “main”: do?\n"]], "corpus": "stackexchange"}
+{"tstamp": 1722525628.145, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722525627.8894, "finish": 1722525628.145, "ip": "", "conv_id": "3265d5ccce70477795f4e5a6efe2da3c", "model_name": "GritLM/GritLM-7B", "prompt": "What's the purpose of `if __name__ == '__main__'`?", "output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: What does `if name == \"__main__\"` mean in Python? \nPossible Duplicate:\nWhat does <if name==\u201cmain\u201d:> do? \n\nI have wrote scripts in Python for quite a while now and I study more of Python as I need it. When reading other people's code I meet if name == \"__main__\": construct quite often. \nWhat is it good for?\n\nA: Basically, \nThere's a distinction between the \"main\" script file and external files which were imported or referenced in another way. If the script is the \"main\" script then the special variable __name__ will equal \"__main__\".\nYou can use this to protect the \"execution\" code from the classes and variables the script has. This enables you to import a script and use classes and variables without actually running that script's main code if it has any.\nSee also: What does if name == \u201cmain\u201d: do?\n"]], "corpus": "stackexchange"}
+{"tstamp": 1722525628.145, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722525627.8894, "finish": 1722525628.145, "ip": "", "conv_id": "1ea6bfbe174d47faa76f5c44c983f261", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "What's the purpose of `if __name__ == '__main__'`?", "output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: What does `if name == \"__main__\"` mean in Python? \nPossible Duplicate:\nWhat does <if name==\u201cmain\u201d:> do? \n\nI have wrote scripts in Python for quite a while now and I study more of Python as I need it. When reading other people's code I meet if name == \"__main__\": construct quite often. \nWhat is it good for?\n\nA: Basically, \nThere's a distinction between the \"main\" script file and external files which were imported or referenced in another way. If the script is the \"main\" script then the special variable __name__ will equal \"__main__\".\nYou can use this to protect the \"execution\" code from the classes and variables the script has. This enables you to import a script and use classes and variables without actually running that script's main code if it has any.\nSee also: What does if name == \u201cmain\u201d: do?\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722525829.799, "task_type": "retrieval", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722525816.4723, "finish": 1722525829.799, "ip": "", "conv_id": "240c4d5107bc4d4c9dc4fdf1bd16fff5", "model_name": "embed-english-v3.0", "prompt": "Where can I find a great tv show about gnomes? They must be real gnomes.", "output": [["Where can I find a great tv show about gnomes? They must be real gnomes.", "Gnome\n\nIn the Warcraft franchise (1994 to present), particularly as featured in the massively multiplayer online role-playing game World of Warcraft, gnomes are a race of beings separate from but allied to dwarves and humans, with whom they share the lands of the Eastern Kingdoms. Crafty, intelligent, and smaller than their dwarven brethren, gnomes are one of two races in Azeroth regarded as technologically savvy. It is suggested in lore that the gnomes originally were mechanical creations that at some point became organic lifeforms. In World of Warcraft, gnomes are an exile race, having irradiated their home city of Gnomeregan in an unsuccessful last-ditch effort to drive out marauding foes.\nMovies\nThe 1967 Walt Disney movie The Gnome-Mobile\nThe 2011 animated movie Gnomeo & Juliet\nThe 2018 animated movie Sherlock Gnomes featured gnomish versions of several classic Sherlock Holmes characters.\nTV Shows\nThe Disney+ Series The Santa Clauses\nThe Little Troll Prince features the troll prince Bu, turning into a gnome and gnomes by the end of the special\nDavid the Gnome , The Wisdom of Gnomes and The New World Of the Gnomes\nDerivative uses\nGarden gnomes\nAfter World War II (with early references, in ironic use, from the late 1930s) the diminutive figurines introduced as lawn ornaments during the 19th century came to be known as garden gnomes. The image of the gnome changed further during the 1960s to 1970s, when the first plastic garden gnomes were manufactured. These gnomes followed the style of the 1937 depiction of the seven dwarves in Snow White and the Seven Dwarfs by Disney.\nThis \"Disneyfied\" image of the gnome was built upon  by the illustrated children's book classic Gnomes (1976), in the original Dutch Leven en werken van de Kabouter, by author Wil Huygen and artist Rien Poortvliet, followed in 1981 by The Secret Book of Gnomes."]], "corpus": "wikipedia"}
-{"tstamp": 1722525829.799, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722525816.4723, "finish": 1722525829.799, "ip": "", "conv_id": "88f733f7082e49fb8da1c2d5a818e7e4", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "Where can I find a great tv show about gnomes? They must be real gnomes.", "output": [["Where can I find a great tv show about gnomes? They must be real gnomes.", "The World of David the Gnome\n\nSynopsis The series follows the gnomes, a kind species who are 15 centimetres (6 inches) tall and weigh between 250 and 300 grams (8 and 10 ounces). There are several types of gnomes: those of the forest, garden, farm, house, dunes, and Siberia, as well as nomadic \"gypsy\" gnomes, who are generally looked down upon by other gnomes. With few exceptions, a gnome's lifespan is exactly 400 years, though one couple in the Balkans lived for 550 years. When they reach the end of their lifespan, they transform into large trees; though they seem to have some control over when and where this happens, they cannot avoid it. For this reason, gnomes are dedicated to protecting forests and the environment, as trees are their ancestors. Gnomes such as the main characters live in pairs in caves or holes under trees, and have a mainly vegetarian diet. They  are often aided by forest animals when traveling long distances or need to reach a specific location quickly. They also have the powers of telepathy and mind control. Their main enemies are the trolls, malevolent and clumsy creatures who cause trouble, as well as gnome poachers.\nCharacters\nDavid (voiced by Tom Bosley) is a gnome of the forest who is 399 years old, making him the oldest gnome around except for Franklin, a gnome from the West who lived for 550 years. David is a doctor and uses his knowledge of many fields, such as hypnosis and acupuncture, to heal his patients, usually animals or other gnomes. David also befriends a bird that, when he whistles, arrives to transport him to where he needs to go. For longer trips, he sometimes travels in a basket attached to its neck.\nLisa (voiced by Jane Woods) is David's wife and companion, who is also 399 years old. They have two children, Lily and Harold. She is in charge of the household, but occasionally accompanies him on his adventures."]], "corpus": "wikipedia"}
+{"tstamp": 1722525829.799, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722525816.4723, "finish": 1722525829.799, "ip": "", "conv_id": "88f733f7082e49fb8da1c2d5a818e7e4", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "Where can I find a great tv show about gnomes? They must be real gnomes.", "output": [["Where can I find a great tv show about gnomes? They must be real gnomes.", "The World of David the Gnome\n\nSynopsis The series follows the gnomes, a kind species who are 15 centimetres (6\u00a0inches) tall and weigh between 250 and 300\u00a0grams (8 and 10 ounces). There are several types of gnomes: those of the forest, garden, farm, house, dunes, and Siberia, as well as nomadic \"gypsy\" gnomes, who are generally looked down upon by other gnomes. With few exceptions, a gnome's lifespan is exactly 400 years, though one couple in the Balkans lived for 550 years. When they reach the end of their lifespan, they transform into large trees; though they seem to have some control over when and where this happens, they cannot avoid it. For this reason, gnomes are dedicated to protecting forests and the environment, as trees are their ancestors. Gnomes such as the main characters live in pairs in caves or holes under trees, and have a mainly vegetarian diet. They  are often aided by forest animals when traveling long distances or need to reach a specific location quickly. They also have the powers of telepathy and mind control. Their main enemies are the trolls, malevolent and clumsy creatures who cause trouble, as well as gnome poachers.\nCharacters\nDavid (voiced by Tom Bosley) is a gnome of the forest who is 399 years old, making him the oldest gnome around except for Franklin, a gnome from the West who lived for 550 years. David is a doctor and uses his knowledge of many fields, such as hypnosis and acupuncture, to heal his patients, usually animals or other gnomes. David also befriends a bird that, when he whistles, arrives to transport him to where he needs to go. For longer trips, he sometimes travels in a basket attached to its neck.\nLisa (voiced by Jane Woods) is David's wife and companion, who is also 399 years old. They have two children, Lily and Harold. She is in charge of the household, but occasionally accompanies him on his adventures."]], "corpus": "wikipedia"}
 {"tstamp": 1722528716.844, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722528716.57, "finish": 1722528716.844, "ip": "", "conv_id": "8505cddd0c2048afa16e19c5ae81488b", "model_name": "GritLM/GritLM-7B", "prompt": "Adversarial example generation for text classification using BERT masked language model  ", "output": [["Adversarial example generation for text classification using BERT masked language model  ", "Title: BAE: BERT-based Adversarial Examples for Text Classification\n\nAbstract: Modern text classification models are susceptible to adversarial examples, perturbed versions of the original text indiscernible by humans which get misclassified by the model. Recent works in NLP use rule-based synonym replacement strategies to generate adversarial examples. These strategies can lead to out-of-context and unnaturally complex token replacements, which are easily identifiable by humans. We present BAE, a black box attack for generating adversarial examples using contextual perturbations from a BERT masked language model. BAE replaces and inserts tokens in the original text by masking a portion of the text and leveraging the BERT-MLM to generate alternatives for the masked tokens. Through automatic and human evaluations, we show that BAE performs a stronger attack, in addition to generating adversarial examples with improved grammaticality and semantic coherence as compared to prior work."]], "corpus": "arxiv"}
 {"tstamp": 1722528716.844, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722528716.57, "finish": 1722528716.844, "ip": "", "conv_id": "2d2bd2bc5a7948898267c5846c7148dc", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "Adversarial example generation for text classification using BERT masked language model  ", "output": [["Adversarial example generation for text classification using BERT masked language model  ", "Title: BAE: BERT-based Adversarial Examples for Text Classification\n\nAbstract: Modern text classification models are susceptible to adversarial examples, perturbed versions of the original text indiscernible by humans which get misclassified by the model. Recent works in NLP use rule-based synonym replacement strategies to generate adversarial examples. These strategies can lead to out-of-context and unnaturally complex token replacements, which are easily identifiable by humans. We present BAE, a black box attack for generating adversarial examples using contextual perturbations from a BERT masked language model. BAE replaces and inserts tokens in the original text by masking a portion of the text and leveraging the BERT-MLM to generate alternatives for the masked tokens. Through automatic and human evaluations, we show that BAE performs a stronger attack, in addition to generating adversarial examples with improved grammaticality and semantic coherence as compared to prior work."]], "corpus": "arxiv"}
 {"tstamp": 1722528729.9611, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722528729.6394, "finish": 1722528729.9611, "ip": "", "conv_id": "36ba38ee6de24dd0a84b4807886838bb", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "which abbott and costello movie has who on first", "output": [["which abbott and costello movie has who on first", "Abbott and Costello\n\nAbbott and Costello made their film debut in the 1940 comedy One Night in the Tropics. The following year, they appeared in three war-themed comedies: Buck Privates, In the Navy, and Keep 'Em Flying. They also appeared in the 1941 horror comedy film Hold That Ghost, and went on to appear in several other horror comedies, including Abbott and Costello Meet Frankenstein (1948), Abbott and Costello Meet the Killer, Boris Karloff (1949), Abbott and Costello Meet the Invisible Man (1951), and Abbott and Costello Meet the Mummy (1955). Other films starring the duo include Pardon My Sarong, Who Done It? (both 1942), The Time of Their Lives (1946), Buck Privates Come Home (1947), Africa Screams (1949), and Abbott and Costello Go to Mars (1953).\nBurlesque\nThe two comedians had crossed paths a few times previously, but first worked together in 1935 at the Eltinge Burlesque Theater on 42nd Street in New York City. Their first performance resulted from Costello's regular partner becoming ill, and Abbott substituting for him.\nOther performers in the show, including Abbott's wife, encouraged a permanent pairing. The duo built an act by refining and reworking numerous burlesque sketches with Abbott as the devious straight man and Costello as the dimwitted comic.\nDecades later, when AMC moved the old theater  further west on 42nd Street to its current location, giant balloons of Abbott and Costello were rigged to appear to pull it.\nRadio\nThe team's first known radio broadcast was on The Kate Smith Hour on February 3, 1938. At first, the similarities between their voices made it difficult for radio listeners (as opposed to stage audiences) to tell them apart during their rapid-fire repartee. As a result, Costello affected a high-pitched, childish voice. \"Who's on First?\" was first performed for a national radio audience the following month. They performed on the program as regulars for two years, while also landing roles in a Broadway revue, The Streets of Paris, in 1939."]], "corpus": "wikipedia"}
 {"tstamp": 1722528729.9611, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722528729.6394, "finish": 1722528729.9611, "ip": "", "conv_id": "df162b5815ef428a8f41297dd9cdaeef", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "which abbott and costello movie has who on first", "output": [["which abbott and costello movie has who on first", "Abbott and Costello\n\nAbbott and Costello made their film debut in the 1940 comedy One Night in the Tropics. The following year, they appeared in three war-themed comedies: Buck Privates, In the Navy, and Keep 'Em Flying. They also appeared in the 1941 horror comedy film Hold That Ghost, and went on to appear in several other horror comedies, including Abbott and Costello Meet Frankenstein (1948), Abbott and Costello Meet the Killer, Boris Karloff (1949), Abbott and Costello Meet the Invisible Man (1951), and Abbott and Costello Meet the Mummy (1955). Other films starring the duo include Pardon My Sarong, Who Done It? (both 1942), The Time of Their Lives (1946), Buck Privates Come Home (1947), Africa Screams (1949), and Abbott and Costello Go to Mars (1953).\nBurlesque\nThe two comedians had crossed paths a few times previously, but first worked together in 1935 at the Eltinge Burlesque Theater on 42nd Street in New York City. Their first performance resulted from Costello's regular partner becoming ill, and Abbott substituting for him.\nOther performers in the show, including Abbott's wife, encouraged a permanent pairing. The duo built an act by refining and reworking numerous burlesque sketches with Abbott as the devious straight man and Costello as the dimwitted comic.\nDecades later, when AMC moved the old theater  further west on 42nd Street to its current location, giant balloons of Abbott and Costello were rigged to appear to pull it.\nRadio\nThe team's first known radio broadcast was on The Kate Smith Hour on February 3, 1938. At first, the similarities between their voices made it difficult for radio listeners (as opposed to stage audiences) to tell them apart during their rapid-fire repartee. As a result, Costello affected a high-pitched, childish voice. \"Who's on First?\" was first performed for a national radio audience the following month. They performed on the program as regulars for two years, while also landing roles in a Broadway revue, The Streets of Paris, in 1939."]], "corpus": "wikipedia"}
-{"tstamp": 1722528760.3394, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722528760.1023, "finish": 1722528760.3394, "ip": "", "conv_id": "2f84ad68265b4681b641e07967d9522a", "model_name": "BM25", "prompt": "are goofy and pluto the same?", "output": [["are goofy and pluto the same?", "Stackoverflow Stackexchange\n\nQ: Input Unicode characters in Pluto.jl Just wondering if anyone knows how to add Unicode input in pluto.jl. For example, if I want to input a Unicode character for sigma, I would type \\sigma in vs code, and the autocomplete will change it to σ. Any ideas how I could do the same in pluto.jl\n\nA: You should be able to do the same in pluto: type \\sigma, then hit TAB and it should change to σ.\n"]], "corpus": "stackexchange"}
+{"tstamp": 1722528760.3394, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722528760.1023, "finish": 1722528760.3394, "ip": "", "conv_id": "2f84ad68265b4681b641e07967d9522a", "model_name": "BM25", "prompt": "are goofy and pluto the same?", "output": [["are goofy and pluto the same?", "Stackoverflow Stackexchange\n\nQ: Input Unicode characters in Pluto.jl Just wondering if anyone knows how to add Unicode input in pluto.jl. For example, if I want to input a Unicode character for sigma, I would type \\sigma in vs code, and the autocomplete will change it to \u03c3. Any ideas how I could do the same in pluto.jl\n\nA: You should be able to do the same in pluto: type \\sigma, then hit TAB and it should change to \u03c3.\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722528760.3394, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722528760.1023, "finish": 1722528760.3394, "ip": "", "conv_id": "b60c4371b328490eb9899fba31918b49", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "are goofy and pluto the same?", "output": [["are goofy and pluto the same?", "Physics Stackexchange\n\nQ: What is the current status of Pluto? Pluto has been designated a planet in our solar system for years (ever since it was discovered in the last century), but in 2006 it was demoted.\nWhat caused this decision? And is there a chance that it could be reversed?\nEdit: well, http://www.dailygalaxy.com/my_weblog/2017/03/nasas-new-horizon-astronomers-declare-pluto-is-a-planet-so-is-jupiters-ocean-moon-europa.html is interesting; this is science, so anything could (potentially) change.\n\nA: Pluto is still considered a dwarf planet. This was because it did not meet the full criteria for being classified as a planet. Most notably it did not clear its orbit of other debris.\nThis is still considered controversial as many scientist do not agree with the definition of what a planet is and still consider Pluto to meet planet criteria. So it is possible that this may change especially if the criteria for what is a planet changes due to new discoveries. This is very possible with the work the Kepler telescope is producing. As we discover new planets in large numbers you can be sure they will be finding ever different planets that will force a redefinition of what a planet is.\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722528930.5625, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722528930.3113, "finish": 1722528930.5625, "ip": "", "conv_id": "0b170e115278407a9b1297593a0ce29f", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "when did they stop cigarette advertising on television", "output": [["when did they stop cigarette advertising on television", "List of cigarette brands\n\nThis is a list of current cigarette brands.  Factory-made cigarettes, when contrasted to roll-your-own cigarettes, are called tailor mades."]], "corpus": "wikipedia"}
-{"tstamp": 1722528930.5625, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722528930.3113, "finish": 1722528930.5625, "ip": "", "conv_id": "fdd4301ff1ef4cbaaee2b80a6d5511a6", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "when did they stop cigarette advertising on television", "output": [["when did they stop cigarette advertising on television", "MCA Inc.\n\nThe Rules were next passed to the 1950s generation of MCA talent agents, including Jerry Perenchio, who later owned and headed a number of businesses including Univision from 1992 to 2007. Perenchio was well known for his version of the Rules (up to twenty rules), which varied from year to year and had some internal contradictions (In 2006, Perenchio pointed out that while there was a \"no nepotism\" rule, he was aware his son was on the company's board of directors at the time).\nMove to Hollywood: \"The Octopus\"\nIn 1939, based on Wasserman's recommendation, MCA's headquarters moved from Chicago to Beverly Hills, California, creating a movie division. The company began to acquire talent agencies, representing established actors such as James Stewart, Henry Fonda, Bette Davis, Jane Wyman and Ronald Reagan, whom Wasserman became very close with personally. In later decades, Wasserman became a guiding force in Reagan's political ambition by helping Reagan to win the presidency of the Screen Actors Guild (SAG), then election as Governor of California in 1966, and finally President of the United States in 1980.\nBy the end of the 1930s, MCA had become the largest talent agency in the world, with over 700 clients, including movie stars, recording artists, Broadway actors, radio stars, and directors. The company's aggressive acquisition of clientele in all entertainment sectors earned MCA the nickname of \"The Octopus\". The company's activities led U.S. Department of Justice agents to investigate not only whether MCA was a monopoly breaking antitrust laws, but also its suspected connections to underworld criminal activities. This investigation continued for the next few decades.\nRevue Productions and the early days of television"]], "corpus": "wikipedia"}
 {"tstamp": 1722528958.355, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722528958.0123, "finish": 1722528958.355, "ip": "", "conv_id": "e76eee5c4d334e1e8457abd9eadb7684", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "Improved YOLOv5 with transformer for drone object detection.  ", "output": [["Improved YOLOv5 with transformer for drone object detection.  ", "Title: TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios\n\nAbstract: Object detection on drone-captured scenarios is a recent popular task. As drones always navigate in different altitudes, the object scale varies violently, which burdens the optimization of networks. Moreover, high-speed and low-altitude flight bring in the motion blur on the densely packed objects, which leads to great challenge of object distinction. To solve the two issues mentioned above, we propose TPH-YOLOv5. Based on YOLOv5, we add one more prediction head to detect different-scale objects. Then we replace the original prediction heads with Transformer Prediction Heads (TPH) to explore the prediction potential with self-attention mechanism. We also integrate convolutional block attention model (CBAM) to find attention region on scenarios with dense objects. To achieve more improvement of our proposed TPH-YOLOv5, we provide bags of useful strategies such as data augmentation, multiscale testing, multi-model integration and utilizing extra classifier. Extensive experiments on dataset VisDrone2021 show that TPH-YOLOv5 have good performance with impressive interpretability on drone-captured scenarios. On DET-test-challenge dataset, the AP result of TPH-YOLOv5 are 39.18%, which is better than previous SOTA method (DPNetV3) by 1.81%. On VisDrone Challenge 2021, TPHYOLOv5 wins 5th place and achieves well-matched results with 1st place model (AP 39.43%). Compared to baseline model (YOLOv5), TPH-YOLOv5 improves about 7%, which is encouraging and competitive."]], "corpus": "arxiv"}
 {"tstamp": 1722528958.355, "task_type": "retrieval", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722528958.0123, "finish": 1722528958.355, "ip": "", "conv_id": "be35da3c45264bda83f9b6682b944d7f", "model_name": "embed-english-v3.0", "prompt": "Improved YOLOv5 with transformer for drone object detection.  ", "output": [["Improved YOLOv5 with transformer for drone object detection.  ", "Title: TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios\n\nAbstract: Object detection on drone-captured scenarios is a recent popular task. As drones always navigate in different altitudes, the object scale varies violently, which burdens the optimization of networks. Moreover, high-speed and low-altitude flight bring in the motion blur on the densely packed objects, which leads to great challenge of object distinction. To solve the two issues mentioned above, we propose TPH-YOLOv5. Based on YOLOv5, we add one more prediction head to detect different-scale objects. Then we replace the original prediction heads with Transformer Prediction Heads (TPH) to explore the prediction potential with self-attention mechanism. We also integrate convolutional block attention model (CBAM) to find attention region on scenarios with dense objects. To achieve more improvement of our proposed TPH-YOLOv5, we provide bags of useful strategies such as data augmentation, multiscale testing, multi-model integration and utilizing extra classifier. Extensive experiments on dataset VisDrone2021 show that TPH-YOLOv5 have good performance with impressive interpretability on drone-captured scenarios. On DET-test-challenge dataset, the AP result of TPH-YOLOv5 are 39.18%, which is better than previous SOTA method (DPNetV3) by 1.81%. On VisDrone Challenge 2021, TPHYOLOv5 wins 5th place and achieves well-matched results with 1st place model (AP 39.43%). Compared to baseline model (YOLOv5), TPH-YOLOv5 improves about 7%, which is encouraging and competitive."]], "corpus": "arxiv"}

data/retrieval_individual-dd51f1d2-bf9d-4e42-9a72-5b30dd2bd338.jsonl

@@ -1,5 +1,4 @@
 {"tstamp": 1722356193.7216, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722356192.9471, "finish": 1722356193.7216, "ip": "", "conv_id": "2719b38b30a44cb08b58c23cfaaac5fe", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "Research improving code generation models using CommitPack and HumanEvalPack datasets.", "output": [["Research improving code generation models using CommitPack and HumanEvalPack datasets.", "Title: Self-Edit: Fault-Aware Code Editor for Code Generation\n\nAbstract: Large language models (LLMs) have demonstrated an impressive ability to generate codes on competitive programming tasks. However, with limited sample numbers, LLMs still suffer from poor accuracy. Inspired by the process of human programming, we propose a generate-and-edit approach named Self-Edit that utilizes execution results of the generated code from LLMs to improve the code quality on the competitive programming task. We execute the generated code on the example test case provided in the question and wrap execution results into a supplementary comment. Utilizing this comment as guidance, our fault-aware code editor is employed to correct errors in the generated code. We perform extensive evaluations across two competitive programming datasets with nine different LLMs. Compared to directly generating from LLMs, our approach can improve the average of pass@1 by 89\\% on APPS-dev, 31\\% on APPS-test, and 48\\% on HumanEval over nine popular code generation LLMs with parameter sizes ranging from 110M to 175B. Compared to other post-processing methods, our method demonstrates superior accuracy and efficiency."]], "corpus": "arxiv"}
-{"tstamp": 1722356836.6481, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722356836.4092, "finish": 1722356836.6481, "ip": "", "conv_id": "bf0d0a6ae2d04641ac0d1978086695e0", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "Research on enhancing Chinese word segmentation by leveraging Bidirectional Encoder Representations from Transformers to handle various annotation criteria across datasets.", "output": [["Research on enhancing Chinese word segmentation by leveraging Bidirectional Encoder Representations from Transformers to handle various annotation criteria across datasets.", "Title: BERT Meets Chinese Word Segmentation\n\nAbstract: Chinese word segmentation (CWS) is a fundamental task for Chinese language understanding. Recently, neural network-based models have attained superior performance in solving the in-domain CWS task. Last year, Bidirectional Encoder Representation from Transformers (BERT), a new language representation model, has been proposed as a backbone model for many natural language tasks and redefined the corresponding performance. The excellent performance of BERT motivates us to apply it to solve the CWS task. By conducting intensive experiments in the benchmark datasets from the second International Chinese Word Segmentation Bake-off, we obtain several keen observations. BERT can slightly improve the performance even when the datasets contain the issue of labeling inconsistency. When applying sufficiently learned features, Softmax, a simpler classifier, can attain the same performance as that of a more complicated classifier, e.g., Conditional Random Field (CRF). The performance of BERT usually increases as the model size increases. The features extracted by BERT can be also applied as good candidates for other neural network models."]], "corpus": "arxiv"}
 {"tstamp": 1722356987.8364, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722356987.4831, "finish": 1722356987.8364, "ip": "", "conv_id": "a59c32d885cb4085888c266c7a858da4", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "trump", "output": [["trump", "Trump\n\nTrump most commonly refers to:\nDonald Trump (born 1946), President of the United States from 2017 to 2021\nTrump (card games), any playing card given an ad-hoc high rank\nTrump may also refer to:\nArts and entertainment\nFilm\nTrump: The Kremlin Candidate?, a 2017 British television film\nTrump: What's the Deal?, an American documentary first screened in 1991\nGaming\nCourt piece or trumps, a trick-taking card game related to whist\nTop Trumps, a card game series\nTrump: The Game, a board game\nMajor Arcana, special cards in a cartomantic Tarot pack, similar to trumps in a normal Tarot pack"]], "corpus": "wikipedia"}
 {"tstamp": 1722357066.1502, "task_type": "retrieval", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722357065.675, "finish": 1722357066.1502, "ip": "", "conv_id": "7c2e24197d4b477499b1f7763539f4a0", "model_name": "embed-english-v3.0", "prompt": "I am looking for a paper to help me deal with data constraints as I scale up my language model.", "output": [["I am looking for a paper to help me deal with data constraints as I scale up my language model.", "Title: Scaling Data-Constrained Language Models\n\nAbstract: The current trend of scaling language models involves increasing both parameter count and training dataset size. Extrapolating this trend suggests that training dataset size may soon be limited by the amount of text data available on the internet. Motivated by this limit, we investigate scaling language models in data-constrained regimes. Specifically, we run a large set of experiments varying the extent of data repetition and compute budget, ranging up to 900 billion training tokens and 9 billion parameter models. We find that with constrained data for a fixed compute budget, training with up to 4 epochs of repeated data yields negligible changes to loss compared to having unique data. However, with more repetition, the value of adding compute eventually decays to zero. We propose and empirically validate a scaling law for compute optimality that accounts for the decreasing value of repeated tokens and excess parameters. Finally, we experiment with approaches mitigating data scarcity, including augmenting the training dataset with code data or removing commonly used filters. Models and datasets from our 400 training runs are freely available at https://github.com/huggingface/datablations."]], "corpus": "arxiv"}
 {"tstamp": 1722357066.1502, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722357065.675, "finish": 1722357066.1502, "ip": "", "conv_id": "d0f4393f2f3b4313a37f5552e2b3c499", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "I am looking for a paper to help me deal with data constraints as I scale up my language model.", "output": [["I am looking for a paper to help me deal with data constraints as I scale up my language model.", "Title: Scaling Data-Constrained Language Models\n\nAbstract: The current trend of scaling language models involves increasing both parameter count and training dataset size. Extrapolating this trend suggests that training dataset size may soon be limited by the amount of text data available on the internet. Motivated by this limit, we investigate scaling language models in data-constrained regimes. Specifically, we run a large set of experiments varying the extent of data repetition and compute budget, ranging up to 900 billion training tokens and 9 billion parameter models. We find that with constrained data for a fixed compute budget, training with up to 4 epochs of repeated data yields negligible changes to loss compared to having unique data. However, with more repetition, the value of adding compute eventually decays to zero. We propose and empirically validate a scaling law for compute optimality that accounts for the decreasing value of repeated tokens and excess parameters. Finally, we experiment with approaches mitigating data scarcity, including augmenting the training dataset with code data or removing commonly used filters. Models and datasets from our 400 training runs are freely available at https://github.com/huggingface/datablations."]], "corpus": "arxiv"}
@@ -25,13 +24,11 @@
 {"tstamp": 1722357648.0654, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722357647.8007, "finish": 1722357648.0654, "ip": "", "conv_id": "40adf2ecfae142f690cf15572d1c2a39", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "A paper with a dataset and methods for Chinese LFQA including real-time web search interaction  ", "output": [["A paper with a dataset and methods for Chinese LFQA including real-time web search interaction  ", "Title: WebCPM: Interactive Web Search for Chinese Long-form Question Answering\n\nAbstract: Long-form question answering (LFQA) aims at answering complex, open-ended questions with detailed, paragraph-length responses. The de facto paradigm of LFQA necessitates two procedures: information retrieval, which searches for relevant supporting facts, and information synthesis, which integrates these facts into a coherent answer. In this paper, we introduce WebCPM, the first Chinese LFQA dataset. One unique feature of WebCPM is that its information retrieval is based on interactive web search, which engages with a search engine in real time. Following WebGPT, we develop a web search interface. We recruit annotators to search for relevant information using our interface and then answer questions. Meanwhile, the web search behaviors of our annotators would be recorded. In total, we collect 5,500 high-quality question-answer pairs, together with 14,315 supporting facts and 121,330 web search actions. We fine-tune pre-trained language models to imitate human behaviors for web search and to generate answers based on the collected facts. Our LFQA pipeline, built on these fine-tuned models, generates answers that are no worse than human-written ones in 32.5% and 47.5% of the cases on our dataset and DuReader, respectively."]], "corpus": "arxiv"}
 {"tstamp": 1722357648.0654, "task_type": "retrieval", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722357647.8007, "finish": 1722357648.0654, "ip": "", "conv_id": "5b7d23084cf84e698b8ae3ef7e10bdc3", "model_name": "embed-english-v3.0", "prompt": "A paper with a dataset and methods for Chinese LFQA including real-time web search interaction  ", "output": [["A paper with a dataset and methods for Chinese LFQA including real-time web search interaction  ", "Title: WebCPM: Interactive Web Search for Chinese Long-form Question Answering\n\nAbstract: Long-form question answering (LFQA) aims at answering complex, open-ended questions with detailed, paragraph-length responses. The de facto paradigm of LFQA necessitates two procedures: information retrieval, which searches for relevant supporting facts, and information synthesis, which integrates these facts into a coherent answer. In this paper, we introduce WebCPM, the first Chinese LFQA dataset. One unique feature of WebCPM is that its information retrieval is based on interactive web search, which engages with a search engine in real time. Following WebGPT, we develop a web search interface. We recruit annotators to search for relevant information using our interface and then answer questions. Meanwhile, the web search behaviors of our annotators would be recorded. In total, we collect 5,500 high-quality question-answer pairs, together with 14,315 supporting facts and 121,330 web search actions. We fine-tune pre-trained language models to imitate human behaviors for web search and to generate answers based on the collected facts. Our LFQA pipeline, built on these fine-tuned models, generates answers that are no worse than human-written ones in 32.5% and 47.5% of the cases on our dataset and DuReader, respectively."]], "corpus": "arxiv"}
 {"tstamp": 1722358062.7324, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722358060.516, "finish": 1722358062.7324, "ip": "", "conv_id": "ba39a43bd1d845a29bb436a813e124f3", "model_name": "text-embedding-004", "prompt": "inpars", "output": [["inpars", "Title: Gamma-Ray Bursts observed by INTEGRAL\n\nAbstract: During the first six months of operations, six Gamma Ray Bursts (GRBs) have been detected in the field of view of the INTEGRAL instruments and localized by the INTEGRAL Burst Alert System (IBAS): a software for the automatic search of GRBs and the rapid distribution of their coordinates. I describe the current performances of IBAS and review the main results obtained so far. The coordinates of the latest burst localized by IBAS, GRB 031203, have been distributed within 20 s from the burst onset and with an uncertainty radius of only 2.7 arcmin."]], "corpus": "arxiv"}
-{"tstamp": 1722358062.7324, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722358060.516, "finish": 1722358062.7324, "ip": "", "conv_id": "613710642ec9426ca02056c89cf5fbf7", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "inpars", "output": [["inpars", "Title: Matrix Model Fixed Point of Noncommutative Phi-Four\n\nAbstract: In this article we exhibit explicitly the matrix model ($\\theta=\\infty$) fixed point of phi-four theory on noncommutative spacetime with only two noncommuting directions using the Wilson renormalization group recursion formula and the 1/N expansion of the zero dimensional reduction and then calculate the mass critical exponent $\\nu$ and the anomalous dimension $\\eta$ in various dimensions ."]], "corpus": "arxiv"}
 {"tstamp": 1722358085.3103, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722358085.114, "finish": 1722358085.3103, "ip": "", "conv_id": "2412f917aa0241cfa42543f4dcf47ff5", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "When did american astronauts first walk on the moon", "output": [["When did american astronauts first walk on the moon", "Exploration of the Moon\n\nIn September 1968 the Soviet Union's Zond 5 sent tortoises on a circumlunar mission, followed by turtles aboard Zond 6 in November. On December 24, 1968, the crew of Apollo 8\u2014Frank Borman, James Lovell and William Anders\u2014became the first human beings to enter lunar orbit and see the far side of the Moon in person. Humans first landed on the Moon on July 20, 1969. The first humans to walk on the lunar surface were Neil Armstrong, commander of the U.S. mission Apollo 11 and his fellow astronaut Buzz Aldrin.\nThe first robot lunar rover to land on the Moon was the Soviet vessel Lunokhod 1 on November 17, 1970, as part of the Lunokhod programme. To date, the last human to stand on the Moon was Eugene Cernan, who as part of the Apollo 17 mission, walked on the Moon in December 1972.\nMoon rock samples were brought back to Earth by three Luna missions (Luna 16, 20, and 24) and the Apollo missions 11 through 17 (except Apollo 13, which aborted its planned lunar landing). Luna 24 in 1976 was the last Lunar mission by either the Soviet Union or the U.S. until Clementine in 1994. Focus shifted to probes to other planets, space stations, and the Shuttle program.\nBefore the \"Moon race,\" the U.S. had pre-projects for scientific and military moonbases: the Lunex Project and Project Horizon. Besides crewed landings, the abandoned Soviet crewed lunar programs included the building of a multipurpose moonbase \"Zvezda\", the first detailed project, complete with developed mockups of expedition vehicles and surface modules.\nAfter 1990"]], "corpus": "wikipedia"}
 {"tstamp": 1722358085.3103, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722358085.114, "finish": 1722358085.3103, "ip": "", "conv_id": "4ca696c1ad1e44dd83714298585835db", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "When did american astronauts first walk on the moon", "output": [["When did american astronauts first walk on the moon", "Walking on the Moon\n\n\"Walking on the Moon\" is a reggae song by British rock band the Police, released as the second single from their second studio album, Reggatta de Blanc (1979). The song was written by the band's lead vocalist and bassist Sting. It went on to become the band's second  hit in the UK.\nBackground\nSting has said that he wrote the song when he was drunk one night after a concert in Munich. The following morning, he remembered the song and wrote it down.\nIn his autobiography, Sting implies that the song was partially inspired by an early girlfriend:\nAccording to Sting, the song was originally recorded \"as a rocker\" in early versions, but it was reworked. The riff, which is played on the bass, was described as \"weird\" and \"jazzy\" by Sting. Guitarist Andy Summers came up with the chord \"which hits after the bass notes\" throughout the song.\n\"Walking on the Moon\" was released as the follow-up single to the British  single \"Message in a Bottle\" in late 1979. The song was the Police's second number-one hit single in the United Kingdom. It also reached  in Ireland and  in Australia but did not chart in the United States."]], "corpus": "wikipedia"}
 {"tstamp": 1722358086.9464, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722358086.7263, "finish": 1722358086.9464, "ip": "", "conv_id": "d192f963eb874f30877d73a20a3386d7", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "are meet or are met?", "output": [["are meet or are met?", "Stackoverflow Stackexchange\n\nQ: Get values from TempData into view I want to get value of TempData into View\nController\n[NonAction]\n    public ActionResult RedirectToMessagePage(string title, string message)\n    {\n        TempData[\"SuccessModel\"] = new Models.SuccessModel { Title = title, Message = message };\n\n        return RedirectToAction(\"Result\", \"Common\");\n    }\n\nView\nif (TempData[\"SuccessModel\"] != null)\n        {\n\n            <div id=\"validationMessages\">\n                @{\n                    var errors =TempData.Values;\n                }\n                @if (errors != null && errors.Count() > 0)\n                {\n                    <div style=\"position:absolute; background:Black; color:White; top:250px; left:550px;\">\n                        @foreach (var error in errors)\n                        {   \n                           @error \n                        }\n                    </div>\n                }\n            </div>\n        }\n\nI want to get value of Message of TempData[\"SuccessModel\"] into view and want to display it.\nHow can I display it?\n\nA: under\nvar errors =TempData.Values;\n\nyou can cast TempData[\"SuccessModel\"] and get it's message\nvar message = (TempData[\"SuccessModel\"] as Models.SuccessModel).Message;\n\nand use this message variable.\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722358086.9464, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722358086.7263, "finish": 1722358086.9464, "ip": "", "conv_id": "f3eef428ee4847acbdadd37fb9b77a6e", "model_name": "BM25", "prompt": "are meet or are met?", "output": [["are meet or are met?", "Ell Stackexchange\n\nQ: \"Meet my boss\" or \"meet with my boss\"? What is the difference between \"meet\" and \"meet with\"?\nFor example, which of these should I say?\n\n  \n*\n  \n*I met my boss yesterday \n  \n*I met with my boss yesterday\n  \n*I'm meeting my friends tonight \n  \n*I'm meeting with my friends tonight\n  \n\n\nA: \nI met my boss yesterday.\n\nWithout elaboration, this implies that you met your boss for the first time.\n\nI met my boss at Spago* yesterday.\n\nYou had lunch or dinner with your boss. (It was not your first encounter.)\n\nI met with my boss yesterday.\n\nYou got together with your boss for a specific purpose. It was likely to be a working meeting of some sort.\n\nI'm meeting my friends tonight.\n\nYou're going out to have fun with your friends.\n\nI'm meeting with my friends tonight.\n\nYou're getting together with friends for some shared purpose. It might be for fun, but the \"with\" is more likely to be used to suggest a special purpose to the meeting: planning, hobby, whatever.\n* Spago is an upscale restaurant, suitable for business luncheons or important dinners.\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722358112.9885, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722358112.8187, "finish": 1722358112.9885, "ip": "", "conv_id": "6e69d5cf6ca64ba795f4d1b52f734ef6", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "inpars", "output": [["inpars", "Title: ParPaRaw: Massively Parallel Parsing of Delimiter-Separated Raw Data\n\nAbstract: Parsing is essential for a wide range of use cases, such as stream processing, bulk loading, and in-situ querying of raw data. Yet, the compute-intense step often constitutes a major bottleneck in the data ingestion pipeline, since parsing of inputs that require more involved parsing rules is challenging to parallelise. This work proposes a massively parallel algorithm for parsing delimiter-separated data formats on GPUs. Other than the state-of-the-art, the proposed approach does not require an initial sequential pass over the input to determine a thread's parsing context. That is, how a thread, beginning somewhere in the middle of the input, should interpret a certain symbol (e.g., whether to interpret a comma as a delimiter or as part of a larger string enclosed in double-quotes). Instead of tailoring the approach to a single format, we are able to perform a massively parallel FSM simulation, which is more flexible and powerful, supporting more expressive parsing rules with general applicability. Achieving a parsing rate of as much as 14.2 GB/s, our experimental evaluation on a GPU with 3584 cores shows that the presented approach is able to scale to thousands of cores and beyond. With an end-to-end streaming approach, we are able to exploit the full-duplex capabilities of the PCIe bus and hide latency from data transfers. Considering the end-to-end performance, the algorithm parses 4.8 GB in as little as 0.44 seconds, including data transfers."]], "corpus": "arxiv"}
-{"tstamp": 1722358112.9885, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722358112.8187, "finish": 1722358112.9885, "ip": "", "conv_id": "f4c133383bd5449c9636e530f8d4fdeb", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "inpars", "output": [["inpars", "Title: Matrix Model Fixed Point of Noncommutative Phi-Four\n\nAbstract: In this article we exhibit explicitly the matrix model ($\\theta=\\infty$) fixed point of phi-four theory on noncommutative spacetime with only two noncommuting directions using the Wilson renormalization group recursion formula and the 1/N expansion of the zero dimensional reduction and then calculate the mass critical exponent $\\nu$ and the anomalous dimension $\\eta$ in various dimensions ."]], "corpus": "arxiv"}
 {"tstamp": 1722358147.5955, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722358147.3814, "finish": 1722358147.5955, "ip": "", "conv_id": "fca24e7cbfba4ce78613bc51183a257c", "model_name": "BM25", "prompt": "inpars", "output": [["inpars", "Title: InPars-v2: Large Language Models as Efficient Dataset Generators for Information Retrieval\n\nAbstract: Recently, InPars introduced a method to efficiently use large language models (LLMs) in information retrieval tasks: via few-shot examples, an LLM is induced to generate relevant queries for documents. These synthetic query-document pairs can then be used to train a retriever. However, InPars and, more recently, Promptagator, rely on proprietary LLMs such as GPT-3 and FLAN to generate such datasets. In this work we introduce InPars-v2, a dataset generator that uses open-source LLMs and existing powerful rerankers to select synthetic query-document pairs for training. A simple BM25 retrieval pipeline followed by a monoT5 reranker finetuned on InPars-v2 data achieves new state-of-the-art results on the BEIR benchmark. To allow researchers to further improve our method, we open source the code, synthetic data, and finetuned models: https://github.com/zetaalphavector/inPars/tree/master/tpu"]], "corpus": "arxiv"}
 {"tstamp": 1722358161.5695, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722358161.3915, "finish": 1722358161.5695, "ip": "", "conv_id": "78dfb52f27f64b61929bcde3dcc9504b", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "inpars", "output": [["inpars", "Title: Phonon Assisted Multimagnon Optical Absorption and Long Lived Two-Magnon States in Undoped Lamellar Copper Oxides\n\nAbstract: We calculate the effective charge for multimagnon infrared (IR) absorption assisted by phonons in the parent insulating compounds of cuprate superconductors and the spectra for two-magnon absorption using interacting spin-wave theory. Recent measured bands in the mid IR [Perkins et al. Phys. Rev. Lett. {\\bf 71} 1621 (1993)] are interpreted as involving one phonon plus a two-magnon virtual bound state, and one phonon plus higher multimagnon absorption processes. The virtual bound state consists of a narrow resonance occurring when the magnon pair has total momentum close to $(\\pi,0)$."]], "corpus": "arxiv"}
 {"tstamp": 1722358161.5695, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722358161.3915, "finish": 1722358161.5695, "ip": "", "conv_id": "59aba873f4524f3298bd6dc89efec421", "model_name": "BM25", "prompt": "inpars", "output": [["inpars", "Title: InPars-v2: Large Language Models as Efficient Dataset Generators for Information Retrieval\n\nAbstract: Recently, InPars introduced a method to efficiently use large language models (LLMs) in information retrieval tasks: via few-shot examples, an LLM is induced to generate relevant queries for documents. These synthetic query-document pairs can then be used to train a retriever. However, InPars and, more recently, Promptagator, rely on proprietary LLMs such as GPT-3 and FLAN to generate such datasets. In this work we introduce InPars-v2, a dataset generator that uses open-source LLMs and existing powerful rerankers to select synthetic query-document pairs for training. A simple BM25 retrieval pipeline followed by a monoT5 reranker finetuned on InPars-v2 data achieves new state-of-the-art results on the BEIR benchmark. To allow researchers to further improve our method, we open source the code, synthetic data, and finetuned models: https://github.com/zetaalphavector/inPars/tree/master/tpu"]], "corpus": "arxiv"}
@@ -77,7 +74,6 @@
 {"tstamp": 1722360172.406, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722360170.2383, "finish": 1722360172.406, "ip": "", "conv_id": "00cd735a722943bca9ff50386cee6ee3", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "Why is the sky blue?", "output": [["Why is the sky blue?", "Rayleigh scattering\n\nRayleigh scattering ( ), named after the 19th-century British physicist Lord Rayleigh (John William Strutt), is the predominantly elastic scattering of light, or other electromagnetic radiation, by particles with a size much smaller than the wavelength of the radiation. For light frequencies well below the resonance frequency of the scattering medium (normal dispersion regime), the amount of scattering is inversely proportional to the fourth power of the wavelength, e.g., a blue color is scattered much more than a red color as light propagates through air.\nRayleigh scattering results from the electric polarizability of the particles. The oscillating electric field of a light wave acts on the charges within a particle, causing them to move at the same frequency. The particle, therefore, becomes a small radiating dipole whose radiation we see as scattered light. The particles may be individual atoms or molecules; it can occur when light travels through transparent solids and liquids, but is most prominently seen in gases.\nRayleigh scattering of sunlight in Earth's atmosphere causes diffuse sky radiation, which is the reason for the blue color of the daytime and twilight sky, as well as the yellowish to reddish hue of the low Sun. Sunlight is also subject to Raman scattering, which changes the rotational state of the molecules and gives rise to polarization effects."]], "corpus": "wikipedia"}
 {"tstamp": 1722360238.9071, "task_type": "retrieval", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722360238.5622, "finish": 1722360238.9071, "ip": "", "conv_id": "1b169b7653ab4c8091ecb4bf186579e2", "model_name": "embed-english-v3.0", "prompt": "Are there grass or fiber eating birds?", "output": [["Are there grass or fiber eating birds?", "Bird\n\nDiet and feeding are varied and often include nectar, fruit, plants, seeds, carrion, and various small animals, including other birds. The digestive system of birds is unique, with a crop for storage and a gizzard that contains swallowed stones for grinding food to compensate for the lack of teeth. Some species such as pigeons and some psittacine species do not have a gallbladder. Most birds are highly adapted for rapid digestion to aid with flight. Some migratory birds have adapted to use protein stored in many parts of their bodies, including protein from the intestines, as additional energy during migration.\nBirds that employ many strategies to obtain food or feed on a variety of food items are called generalists, while others that concentrate time and effort on specific food items or have a single strategy to obtain food are considered specialists. Avian foraging strategies can vary widely by species. Many birds glean for insects, invertebrates, fruit, or seeds. Some hunt insects by suddenly attacking from a branch. Those species that seek pest insects are considered beneficial 'biological control agents' and their presence encouraged in biological pest control programmes. Combined, insectivorous birds eat 400\u2013500 million metric tons of arthropods annually.\nNectar feeders such as hummingbirds, sunbirds, lories, and lorikeets amongst others have specially adapted brushy tongues and in many cases bills designed to fit co-adapted flowers. Kiwis and shorebirds with long bills probe for invertebrates; shorebirds' varied bill lengths and feeding methods result in the separation of ecological niches. Loons, diving ducks, penguins and auks pursue their prey underwater, using their wings or feet for propulsion, while aerial predators such as sulids, kingfishers and terns plunge dive after their prey. Flamingos, three species of prion, and some ducks are filter feeders. Geese and dabbling ducks are primarily grazers."]], "corpus": "wikipedia"}
 {"tstamp": 1722360238.9071, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722360238.5622, "finish": 1722360238.9071, "ip": "", "conv_id": "bf7123c3bf574070873b3438c66a7145", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "Are there grass or fiber eating birds?", "output": [["Are there grass or fiber eating birds?", "Bird\n\nDiet and feeding are varied and often include nectar, fruit, plants, seeds, carrion, and various small animals, including other birds. The digestive system of birds is unique, with a crop for storage and a gizzard that contains swallowed stones for grinding food to compensate for the lack of teeth. Some species such as pigeons and some psittacine species do not have a gallbladder. Most birds are highly adapted for rapid digestion to aid with flight. Some migratory birds have adapted to use protein stored in many parts of their bodies, including protein from the intestines, as additional energy during migration.\nBirds that employ many strategies to obtain food or feed on a variety of food items are called generalists, while others that concentrate time and effort on specific food items or have a single strategy to obtain food are considered specialists. Avian foraging strategies can vary widely by species. Many birds glean for insects, invertebrates, fruit, or seeds. Some hunt insects by suddenly attacking from a branch. Those species that seek pest insects are considered beneficial 'biological control agents' and their presence encouraged in biological pest control programmes. Combined, insectivorous birds eat 400\u2013500 million metric tons of arthropods annually.\nNectar feeders such as hummingbirds, sunbirds, lories, and lorikeets amongst others have specially adapted brushy tongues and in many cases bills designed to fit co-adapted flowers. Kiwis and shorebirds with long bills probe for invertebrates; shorebirds' varied bill lengths and feeding methods result in the separation of ecological niches. Loons, diving ducks, penguins and auks pursue their prey underwater, using their wings or feet for propulsion, while aerial predators such as sulids, kingfishers and terns plunge dive after their prey. Flamingos, three species of prion, and some ducks are filter feeders. Geese and dabbling ducks are primarily grazers."]], "corpus": "wikipedia"}
-{"tstamp": 1722360288.8895, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722360288.4132, "finish": 1722360288.8895, "ip": "", "conv_id": "b454985e65da4a54967a4a82ac91170c", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "Can plants get cancer?", "output": [["Can plants get cancer?", "Blue agave\n\nTequila production Tequila is made only from a specific cultivar of Agave tequilana called 'Weber Azul'. The plant is from the Asparagaceae family. This cultivar is larger and blue-gray in color compared to the smaller and green normal A.\u00a0tequilana. It is a rapid grower and prolific offsetter in keeping with its agricultural advantages. Tequila is produced by removing the heart (pi\u00f1a) of the plant in its seventh to fourteenth year (depending on growth rate and whims of harvester). Harvested pi\u00f1as normally weigh . This heart is stripped of its leaves and heated to convert the inulin to sugars. Then the roasted core is pressed or crushed to release the sugary clear liquid called aguamiel, which is, in turn, fermented and distilled into alcohol. Tequila is also made with a sugar formulation of 51% agave and 49% other sugars. These tequilas are referred to as Mixtos.\nPathogens affecting the plant\nAs agave production has moved to an industrial scale since the end of the 1980s, diseases and pests, collectively referred to as TMA (tristeza y muerte de agave, \"wilting and death of agave\"), have hit the crops. Through the 1990s, diseases spread, particularly Fusarium fungi and Erwinia bacteria, exacerbated by the low genetic diversity of the agave plants. Other problems include the agave weevil, Scyphophorus acupunctatus, and a fungus, Thielaviopsis paradoxa.\nAccording to a 2004 study, additional pathogens, Erwinia carotovora, Enterobacter agglomerans, Pseudomonas mendocina, and Serratia spp. are responsible for continued rot."]], "corpus": "wikipedia"}
 {"tstamp": 1722360288.8895, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722360288.4132, "finish": 1722360288.8895, "ip": "", "conv_id": "8e0a2106198b416899abf77d45576c31", "model_name": "voyage-multilingual-2", "prompt": "Can plants get cancer?", "output": [["Can plants get cancer?", "List of unproven and disproven cancer treatments\n\nVenus flytrap \u2013 a carnivorous plant, the extract of which is promoted as a treatment for a variety of human ailments including skin cancer. According to the American Cancer Society, \"available scientific evidence does not support claims that extract from the Venus flytrap plant is effective in treating skin cancer or any other type of cancer\".\nWalnuts \u2013 large, hard edible seeds of any tree of the genus Juglans. Black walnut has been promoted as a cancer cure on the basis it kills a \"parasite\" responsible for the disease. However, according to the American Cancer Society, \"available scientific evidence does not support claims that hulls from black walnuts remove parasites from the intestinal tract or that they are effective in treating cancer or any other disease\".\nWheatgrass \u2013 a food made from grains of wheat. According to the American Cancer Society, although some wheatgrass champions claim it can \"shrink\" cancer tumors, \"available scientific evidence does not support the idea that wheatgrass or the wheatgrass diet can cure or prevent disease\".\nWild yam (or Chinese yam) \u2013 types of yam, the roots of which are made into creams and dietary supplements that are promoted for a variety of medicinal purposes, including cancer prevention. The American Cancer Society says of these products, \"available scientific evidence does not support claims that they are safe or effective.\""]], "corpus": "wikipedia"}
 {"tstamp": 1722360773.5123, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722360773.3559, "finish": 1722360773.5123, "ip": "", "conv_id": "c98a7a4397b144d580661f955970d07a", "model_name": "BM25", "prompt": "What is I para?", "output": [["What is I para?", "Title: Adsorption of para-Hydrogen on Krypton pre-plated graphite\n\nAbstract: Adsorption of para-Hydrogen on the surface of graphite pre-plated with a single layer of atomic krypton is studied thoretically by means of Path Integral Ground State Monte Carlo simulations. We compute energetics and density profiles of para-hydrogen, and determine the structure of the adsorbed film for various coverages. Results show that there are two thermodynamically stable monolayer phases of para-hydrogen, both solid. One is commensurate with the krypton layer, the other is incommensurate. No evidence is seen of a thermodynamically stable liquid phase, at zero temperature. These results are qualitatively similar to what is seen for for para-hydrogen on bare graphite. Quantum exchanges of hydrogen molecules are suppressed in this system."]], "corpus": "arxiv"}
 {"tstamp": 1722360943.2037, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722360943.1125, "finish": 1722360943.2037, "ip": "", "conv_id": "ea89ad4b67514cca9ee93d3de356758b", "model_name": "BM25", "prompt": "What is inpars?", "output": [["What is inpars?", "Title: InPars-v2: Large Language Models as Efficient Dataset Generators for Information Retrieval\n\nAbstract: Recently, InPars introduced a method to efficiently use large language models (LLMs) in information retrieval tasks: via few-shot examples, an LLM is induced to generate relevant queries for documents. These synthetic query-document pairs can then be used to train a retriever. However, InPars and, more recently, Promptagator, rely on proprietary LLMs such as GPT-3 and FLAN to generate such datasets. In this work we introduce InPars-v2, a dataset generator that uses open-source LLMs and existing powerful rerankers to select synthetic query-document pairs for training. A simple BM25 retrieval pipeline followed by a monoT5 reranker finetuned on InPars-v2 data achieves new state-of-the-art results on the BEIR benchmark. To allow researchers to further improve our method, we open source the code, synthetic data, and finetuned models: https://github.com/zetaalphavector/inPars/tree/master/tpu"]], "corpus": "arxiv"}

data/retrieval_individual-f0f24c65-6f65-4435-a1e5-20bb12a2b295.jsonl

@@ -43,7 +43,6 @@
 {"tstamp": 1722224784.465, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722224781.7765, "finish": 1722224784.465, "ip": "", "conv_id": "44bd3abae6e44faba2d1cf54ce8bec32", "model_name": "text-embedding-004", "prompt": "A paper evaluating whether updating one fact in a language model affects related facts.", "output": [["A paper evaluating whether updating one fact in a language model affects related facts.", "Title: WikiFactDiff: A Large, Realistic, and Temporally Adaptable Dataset for Atomic Factual Knowledge Update in Causal Language Models\n\nAbstract: The factuality of large language model (LLMs) tends to decay over time since events posterior to their training are \"unknown\" to them. One way to keep models up-to-date could be factual update: the task of inserting, replacing, or removing certain simple (atomic) facts within the model. To study this task, we present WikiFactDiff, a dataset that describes the evolution of factual knowledge between two dates as a collection of simple facts divided into three categories: new, obsolete, and static. We describe several update scenarios arising from various combinations of these three types of basic update. The facts are represented by subject-relation-object triples; indeed, WikiFactDiff was constructed by comparing the state of the Wikidata knowledge base at 4 January 2021 and 27 February 2023. Those fact are accompanied by verbalization templates and cloze tests that enable running update algorithms and their evaluation metrics. Contrary to other datasets, such as zsRE and CounterFact, WikiFactDiff constitutes a realistic update setting that involves various update scenarios, including replacements, archival, and new entity insertions. We also present an evaluation of existing update algorithms on WikiFactDiff."]], "corpus": "arxiv"}
 {"tstamp": 1722224802.5928, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722224802.3639, "finish": 1722224802.5928, "ip": "", "conv_id": "c442c289c7594bd7839f1f8bafc30f60", "model_name": "BM25", "prompt": "I'm looking for a QA model that retrieves both textual and visual information.  ", "output": [["I'm looking for a QA model that retrieves both textual and visual information.  ", "Title: Look Before you Speak: Visually Contextualized Utterances\n\nAbstract: While most conversational AI systems focus on textual dialogue only, conditioning utterances on visual context (when it's available) can lead to more realistic conversations. Unfortunately, a major challenge for incorporating visual context into conversational dialogue is the lack of large-scale labeled datasets. We provide a solution in the form of a new visually conditioned Future Utterance Prediction task. Our task involves predicting the next utterance in a video, using both visual frames and transcribed speech as context. By exploiting the large number of instructional videos online, we train a model to solve this task at scale, without the need for manual annotations. Leveraging recent advances in multimodal learning, our model consists of a novel co-attentional multimodal video transformer, and when trained on both textual and visual context, outperforms baselines that use textual inputs alone. Further, we demonstrate that our model trained for this task on unlabelled videos achieves state-of-the-art performance on a number of downstream VideoQA benchmarks such as MSRVTT-QA, MSVD-QA, ActivityNet-QA and How2QA."]], "corpus": "arxiv"}
 {"tstamp": 1722224802.5928, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722224802.3639, "finish": 1722224802.5928, "ip": "", "conv_id": "303ffd802de74cccb01c62b371bfd341", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "I'm looking for a QA model that retrieves both textual and visual information.  ", "output": [["I'm looking for a QA model that retrieves both textual and visual information.  ", "Title: Question Aware Vision Transformer for Multimodal Reasoning\n\nAbstract: Vision-Language (VL) models have gained significant research focus, enabling remarkable advances in multimodal reasoning. These architectures typically comprise a vision encoder, a Large Language Model (LLM), and a projection module that aligns visual features with the LLM's representation space. Despite their success, a critical limitation persists: the vision encoding process remains decoupled from user queries, often in the form of image-related questions. Consequently, the resulting visual features may not be optimally attuned to the query-specific elements of the image. To address this, we introduce QA-ViT, a Question Aware Vision Transformer approach for multimodal reasoning, which embeds question awareness directly within the vision encoder. This integration results in dynamic visual features focusing on relevant image aspects to the posed question. QA-ViT is model-agnostic and can be incorporated efficiently into any VL architecture. Extensive experiments demonstrate the effectiveness of applying our method to various multimodal architectures, leading to consistent improvement across diverse tasks and showcasing its potential for enhancing visual and scene-text understanding."]], "corpus": "arxiv"}
-{"tstamp": 1722224861.2135, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722224831.7718, "finish": 1722224861.2135, "ip": "", "conv_id": "66d97eb1cd7d4e31b48bcf9e823bba43", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "A method using descriptors from large language models to improve accuracy and mitigate bias in visual classification tasks.", "output": [["A method using descriptors from large language models to improve accuracy and mitigate bias in visual classification tasks.", "Title: Images in Language Space: Exploring the Suitability of Large Language Models for Vision & Language Tasks\n\nAbstract: Large language models have demonstrated robust performance on various language tasks using zero-shot or few-shot learning paradigms. While being actively researched, multimodal models that can additionally handle images as input have yet to catch up in size and generality with language-only models. In this work, we ask whether language-only models can be utilised for tasks that require visual input -- but also, as we argue, often require a strong reasoning component. Similar to some recent related work, we make visual information accessible to the language model using separate verbalisation models. Specifically, we investigate the performance of open-source, open-access language models against GPT-3 on five vision-language tasks when given textually-encoded visual information. Our results suggest that language models are effective for solving vision-language tasks even with limited samples. This approach also enhances the interpretability of a model's output by providing a means of tracing the output back through the verbalised image content."]], "corpus": "arxiv"}
 {"tstamp": 1722224861.2135, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722224831.7718, "finish": 1722224861.2135, "ip": "", "conv_id": "e97d1a7a80bf4651a54001a5a1acb3b0", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "A method using descriptors from large language models to improve accuracy and mitigate bias in visual classification tasks.", "output": [["A method using descriptors from large language models to improve accuracy and mitigate bias in visual classification tasks.", "Title: Visual Classification via Description from Large Language Models\n\nAbstract: Vision-language models (VLMs) such as CLIP have shown promising performance on a variety of recognition tasks using the standard zero-shot classification procedure -- computing similarity between the query image and the embedded words for each category. By only using the category name, they neglect to make use of the rich context of additional information that language affords. The procedure gives no intermediate understanding of why a category is chosen, and furthermore provides no mechanism for adjusting the criteria used towards this decision. We present an alternative framework for classification with VLMs, which we call classification by description. We ask VLMs to check for descriptive features rather than broad categories: to find a tiger, look for its stripes; its claws; and more. By basing decisions on these descriptors, we can provide additional cues that encourage using the features we want to be used. In the process, we can get a clear idea of what features the model uses to construct its decision; it gains some level of inherent explainability. We query large language models (e.g., GPT-3) for these descriptors to obtain them in a scalable way. Extensive experiments show our framework has numerous advantages past interpretability. We show improvements in accuracy on ImageNet across distribution shifts; demonstrate the ability to adapt VLMs to recognize concepts unseen during training; and illustrate how descriptors can be edited to effectively mitigate bias compared to the baseline."]], "corpus": "arxiv"}
 {"tstamp": 1722224916.3804, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722224906.02, "finish": 1722224916.3804, "ip": "", "conv_id": "04682ef77bb14d3d987fa49503bd1c7b", "model_name": "voyage-multilingual-2", "prompt": "Highest BLEU scores for fully NAT in WMT14 En-De with faster inference.", "output": [["Highest BLEU scores for fully NAT in WMT14 En-De with faster inference.", "Title: Fully Non-autoregressive Neural Machine Translation: Tricks of the Trade\n\nAbstract: Fully non-autoregressive neural machine translation (NAT) is proposed to simultaneously predict tokens with single forward of neural networks, which significantly reduces the inference latency at the expense of quality drop compared to the Transformer baseline. In this work, we target on closing the performance gap while maintaining the latency advantage. We first inspect the fundamental issues of fully NAT models, and adopt dependency reduction in the learning space of output tokens as the basic guidance. Then, we revisit methods in four different aspects that have been proven effective for improving NAT models, and carefully combine these techniques with necessary modifications. Our extensive experiments on three translation benchmarks show that the proposed system achieves the new state-of-the-art results for fully NAT models, and obtains comparable performance with the autoregressive and iterative NAT systems. For instance, one of the proposed models achieves 27.49 BLEU points on WMT14 En-De with approximately 16.5X speed up at inference time."]], "corpus": "arxiv"}
 {"tstamp": 1722224916.3804, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722224906.02, "finish": 1722224916.3804, "ip": "", "conv_id": "e6e5e63a277c497b87192e49b67e8463", "model_name": "text-embedding-004", "prompt": "Highest BLEU scores for fully NAT in WMT14 En-De with faster inference.", "output": [["Highest BLEU scores for fully NAT in WMT14 En-De with faster inference.", "Title: Fully Non-autoregressive Neural Machine Translation: Tricks of the Trade\n\nAbstract: Fully non-autoregressive neural machine translation (NAT) is proposed to simultaneously predict tokens with single forward of neural networks, which significantly reduces the inference latency at the expense of quality drop compared to the Transformer baseline. In this work, we target on closing the performance gap while maintaining the latency advantage. We first inspect the fundamental issues of fully NAT models, and adopt dependency reduction in the learning space of output tokens as the basic guidance. Then, we revisit methods in four different aspects that have been proven effective for improving NAT models, and carefully combine these techniques with necessary modifications. Our extensive experiments on three translation benchmarks show that the proposed system achieves the new state-of-the-art results for fully NAT models, and obtains comparable performance with the autoregressive and iterative NAT systems. For instance, one of the proposed models achieves 27.49 BLEU points on WMT14 En-De with approximately 16.5X speed up at inference time."]], "corpus": "arxiv"}
@@ -61,7 +60,6 @@
 {"tstamp": 1722225056.0879, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722225047.1129, "finish": 1722225056.0879, "ip": "", "conv_id": "6766409719cd420188c069799f6c48d1", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "can r squared adjusted be negative?", "output": [["can r squared adjusted be negative?", "Stats Stackexchange\n\nQ: Is it a problem to get a negative adjusted r-squared? Background:\nI have the cross-sectional model:\n$Y_{i}  = a + b X_{1,i} + c X_{2,i} + d X_{3,i} + e X_{4,i} + \\nu_i$.\nThe application is corporate finance. So each $Y_i$ is something like the change in return on assets over a 1 year period for firm $i$, and the regressors are typical corporate finance variables.\nIn corporate finance, very small values of $R^2$ are common, even sometimes $1\\%$. My $R^2$ is around $1\\%$ but my Adjusted $R^2$ is $-0.2\\%$. \nI have never seen papers report negative Adjusted $R^2$ but this could just be because they omit the publication of their adjusted $R^2$ when they see that it is negative. \nQuestion\nIs there some problem when the adjusted $R^2$ is negative?\n\nA: The formula for adjusted R square allows it to be negative.  It is intended to approximate the actual percentage variance explained.  So if the actual R square is close to zero the adjusted R square can be slightly negative.  Just think of it as an estimate of zero.\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722225074.1698, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722225073.7441, "finish": 1722225074.1698, "ip": "", "conv_id": "fe65d33c55ca4dbf856b34d9fd465f40", "model_name": "text-embedding-3-large", "prompt": "Paper showcasing reinforcement learning from human feedback to train beneficial and safe AI assistants.", "output": [["Paper showcasing reinforcement learning from human feedback to train beneficial and safe AI assistants.", "Title: Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback\n\nAbstract: We apply preference modeling and reinforcement learning from human feedback (RLHF) to finetune language models to act as helpful and harmless assistants. We find this alignment training improves performance on almost all NLP evaluations, and is fully compatible with training for specialized skills such as python coding and summarization. We explore an iterated online mode of training, where preference models and RL policies are updated on a weekly cadence with fresh human feedback data, efficiently improving our datasets and models. Finally, we investigate the robustness of RLHF training, and identify a roughly linear relation between the RL reward and the square root of the KL divergence between the policy and its initialization. Alongside our main results, we perform peripheral analyses on calibration, competing objectives, and the use of OOD detection, compare our models with human writers, and provide samples from our models using prompts appearing in recent related work."]], "corpus": "arxiv"}
 {"tstamp": 1722225074.1698, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722225073.7441, "finish": 1722225074.1698, "ip": "", "conv_id": "19f1b179abd4428983f12ecbb76ba96e", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "Paper showcasing reinforcement learning from human feedback to train beneficial and safe AI assistants.", "output": [["Paper showcasing reinforcement learning from human feedback to train beneficial and safe AI assistants.", "Title: Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback\n\nAbstract: We apply preference modeling and reinforcement learning from human feedback (RLHF) to finetune language models to act as helpful and harmless assistants. We find this alignment training improves performance on almost all NLP evaluations, and is fully compatible with training for specialized skills such as python coding and summarization. We explore an iterated online mode of training, where preference models and RL policies are updated on a weekly cadence with fresh human feedback data, efficiently improving our datasets and models. Finally, we investigate the robustness of RLHF training, and identify a roughly linear relation between the RL reward and the square root of the KL divergence between the policy and its initialization. Alongside our main results, we perform peripheral analyses on calibration, competing objectives, and the use of OOD detection, compare our models with human writers, and provide samples from our models using prompts appearing in recent related work."]], "corpus": "arxiv"}
-{"tstamp": 1722225091.4142, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722225089.0182, "finish": 1722225091.4142, "ip": "", "conv_id": "94d522a8ae794130b5549c97be9efcc8", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "Looking for a paper on Fine-grained Entity Type Classification using a neural network with hierarchy-aware loss.", "output": [["Looking for a paper on Fine-grained Entity Type Classification using a neural network with hierarchy-aware loss.", "Title: Graph Neural Network Approach to Semantic Type Detection in Tables\n\nAbstract: This study addresses the challenge of detecting semantic column types in relational tables, a key task in many real-world applications. While language models like BERT have improved prediction accuracy, their token input constraints limit the simultaneous processing of intra-table and inter-table information. We propose a novel approach using Graph Neural Networks (GNNs) to model intra-table dependencies, allowing language models to focus on inter-table information. Our proposed method not only outperforms existing state-of-the-art algorithms but also offers novel insights into the utility and functionality of various GNN types for semantic type detection. The code is available at https://github.com/hoseinzadeehsan/GAIT"]], "corpus": "arxiv"}
 {"tstamp": 1722225091.4142, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722225089.0182, "finish": 1722225091.4142, "ip": "", "conv_id": "5bc98453ebd94dd8adc5a3a7477560d1", "model_name": "GritLM/GritLM-7B", "prompt": "Looking for a paper on Fine-grained Entity Type Classification using a neural network with hierarchy-aware loss.", "output": [["Looking for a paper on Fine-grained Entity Type Classification using a neural network with hierarchy-aware loss.", "Title: Neural Fine-Grained Entity Type Classification with Hierarchy-Aware Loss\n\nAbstract: The task of Fine-grained Entity Type Classification (FETC) consists of assigning types from a hierarchy to entity mentions in text. Existing methods rely on distant supervision and are thus susceptible to noisy labels that can be out-of-context or overly-specific for the training sentence. Previous methods that attempt to address these issues do so with heuristics or with the help of hand-crafted features. Instead, we propose an end-to-end solution with a neural network model that uses a variant of cross- entropy loss function to handle out-of-context labels, and hierarchical loss normalization to cope with overly-specific ones. Also, previous work solve FETC a multi-label classification followed by ad-hoc post-processing. In contrast, our solution is more elegant: we use public word embeddings to train a single-label that jointly learns representations for entity mentions and their context. We show experimentally that our approach is robust against noise and consistently outperforms the state-of-the-art on established benchmarks for the task."]], "corpus": "arxiv"}
 {"tstamp": 1722225117.8002, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722225117.6315, "finish": 1722225117.8002, "ip": "", "conv_id": "61042e3396c64d919604031cbf9612dc", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "A paper introducing a new large language model", "output": [["A paper introducing a new large language model", "Title: Larger-Context Language Modelling\n\nAbstract: In this work, we propose a novel method to incorporate corpus-level discourse information into language modelling. We call this larger-context language model. We introduce a late fusion approach to a recurrent language model based on long short-term memory units (LSTM), which helps the LSTM unit keep intra-sentence dependencies and inter-sentence dependencies separate from each other. Through the evaluation on three corpora (IMDB, BBC, and PennTree Bank), we demon- strate that the proposed model improves perplexity significantly. In the experi- ments, we evaluate the proposed approach while varying the number of context sentences and observe that the proposed late fusion is superior to the usual way of incorporating additional inputs to the LSTM. By analyzing the trained larger- context language model, we discover that content words, including nouns, adjec- tives and verbs, benefit most from an increasing number of context sentences. This analysis suggests that larger-context language model improves the unconditional language model by capturing the theme of a document better and more easily."]], "corpus": "arxiv"}
 {"tstamp": 1722225117.8002, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722225117.6315, "finish": 1722225117.8002, "ip": "", "conv_id": "d2e0f34f14254329b828836aa5ea813e", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "A paper introducing a new large language model", "output": [["A paper introducing a new large language model", "Title: Large Language Models: A Survey\n\nAbstract: Large Language Models (LLMs) have drawn a lot of attention due to their strong performance on a wide range of natural language tasks, since the release of ChatGPT in November 2022. LLMs' ability of general-purpose language understanding and generation is acquired by training billions of model's parameters on massive amounts of text data, as predicted by scaling laws \\cite{kaplan2020scaling,hoffmann2022training}. The research area of LLMs, while very recent, is evolving rapidly in many different ways. In this paper, we review some of the most prominent LLMs, including three popular LLM families (GPT, LLaMA, PaLM), and discuss their characteristics, contributions and limitations. We also give an overview of techniques developed to build, and augment LLMs. We then survey popular datasets prepared for LLM training, fine-tuning, and evaluation, review widely used LLM evaluation metrics, and compare the performance of several popular LLMs on a set of representative benchmarks. Finally, we conclude the paper by discussing open challenges and future research directions."]], "corpus": "arxiv"}
@@ -80,7 +78,6 @@
 {"tstamp": 1722226579.6683, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722226570.6133, "finish": 1722226579.6683, "ip": "", "conv_id": "f0dfad42986f425e91134329193506e8", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "is bloodborne in the same universe as dark souls?", "output": [["is bloodborne in the same universe as dark souls?", "Stackoverflow Stackexchange\n\nQ: Stop UIAlertView from dismissing Can you stop UIAlertView from dismissing? \nI want based on what button is pressed, to either animate adding a UITextField, or dismiss it. But I can't add a text field after the alert is dismissed.\n\nA: You should implement the UIAlertViewDelegate method :\n- (void)alertView:(UIAlertView *)alertView willDismissWithButtonIndex:(NSInteger)buttonIndex\n\nand based on the button index you can perform your custom action.\nOtherwise you should either create your own alertview class or subclass UIAlertView.\nIf you choose to subclass you can override the method : \n-(void)dismissWithClickedButtonIndex:(NSInteger)buttonIndex animated:(BOOL)animated \n\nto avoid dismissing the alertView if a specific button is pressed\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722226600.4022, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722226599.9625, "finish": 1722226600.4022, "ip": "", "conv_id": "febd759447c24fbbab98fe669530ae5b", "model_name": "voyage-multilingual-2", "prompt": "How well do ChatGPT and GPT-4 perform on logical reasoning datasets?", "output": [["How well do ChatGPT and GPT-4 perform on logical reasoning datasets?", "Title: Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4\n\nAbstract: Harnessing logical reasoning ability is a comprehensive natural language understanding endeavor. With the release of Generative Pretrained Transformer 4 (GPT-4), highlighted as \"advanced\" at reasoning tasks, we are eager to learn the GPT-4 performance on various logical reasoning tasks. This report analyses multiple logical reasoning datasets, with popular benchmarks like LogiQA and ReClor, and newly-released datasets like AR-LSAT. We test the multi-choice reading comprehension and natural language inference tasks with benchmarks requiring logical reasoning. We further construct a logical reasoning out-of-distribution dataset to investigate the robustness of ChatGPT and GPT-4. We also make a performance comparison between ChatGPT and GPT-4. Experiment results show that ChatGPT performs significantly better than the RoBERTa fine-tuning method on most logical reasoning benchmarks. With early access to the GPT-4 API we are able to conduct intense experiments on the GPT-4 model. The results show GPT-4 yields even higher performance on most logical reasoning datasets. Among benchmarks, ChatGPT and GPT-4 do relatively well on well-known datasets like LogiQA and ReClor. However, the performance drops significantly when handling newly released and out-of-distribution datasets. Logical reasoning remains challenging for ChatGPT and GPT-4, especially on out-of-distribution and natural language inference datasets. We release the prompt-style logical reasoning datasets as a benchmark suite and name it LogiEval."]], "corpus": "arxiv"}
 {"tstamp": 1722226600.4022, "task_type": "retrieval", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722226599.9625, "finish": 1722226600.4022, "ip": "", "conv_id": "eac6e53b37a34cda9371c8b5695b6794", "model_name": "embed-english-v3.0", "prompt": "How well do ChatGPT and GPT-4 perform on logical reasoning datasets?", "output": [["How well do ChatGPT and GPT-4 perform on logical reasoning datasets?", "Title: Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4\n\nAbstract: Harnessing logical reasoning ability is a comprehensive natural language understanding endeavor. With the release of Generative Pretrained Transformer 4 (GPT-4), highlighted as \"advanced\" at reasoning tasks, we are eager to learn the GPT-4 performance on various logical reasoning tasks. This report analyses multiple logical reasoning datasets, with popular benchmarks like LogiQA and ReClor, and newly-released datasets like AR-LSAT. We test the multi-choice reading comprehension and natural language inference tasks with benchmarks requiring logical reasoning. We further construct a logical reasoning out-of-distribution dataset to investigate the robustness of ChatGPT and GPT-4. We also make a performance comparison between ChatGPT and GPT-4. Experiment results show that ChatGPT performs significantly better than the RoBERTa fine-tuning method on most logical reasoning benchmarks. With early access to the GPT-4 API we are able to conduct intense experiments on the GPT-4 model. The results show GPT-4 yields even higher performance on most logical reasoning datasets. Among benchmarks, ChatGPT and GPT-4 do relatively well on well-known datasets like LogiQA and ReClor. However, the performance drops significantly when handling newly released and out-of-distribution datasets. Logical reasoning remains challenging for ChatGPT and GPT-4, especially on out-of-distribution and natural language inference datasets. We release the prompt-style logical reasoning datasets as a benchmark suite and name it LogiEval."]], "corpus": "arxiv"}
-{"tstamp": 1722226615.219, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722226612.9917, "finish": 1722226615.219, "ip": "", "conv_id": "fac186d006924c3d877e7829c4683200", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "Language models fine-tuning for non-language tasks without architectural changes", "output": [["Language models fine-tuning for non-language tasks without architectural changes", "Title: Linear Transformers with Learnable Kernel Functions are Better In-Context Models\n\nAbstract: Advancing the frontier of subquadratic architectures for Language Models (LMs) is crucial in the rapidly evolving field of natural language processing. Current innovations, including State Space Models, were initially celebrated for surpassing Transformer performance on language modeling tasks. However, these models have revealed deficiencies in essential In-Context Learning capabilities - a domain where the Transformer traditionally shines. The Based model emerged as a hybrid solution, blending a Linear Transformer with a kernel inspired by the Taylor expansion of exponential functions, augmented by convolutional networks. Mirroring the Transformer's in-context adeptness, it became a strong contender in the field. In our work, we present a singular, elegant alteration to the Based kernel that amplifies its In-Context Learning abilities evaluated with the Multi-Query Associative Recall task and overall language modeling process, as demonstrated on the Pile dataset."]], "corpus": "arxiv"}
 {"tstamp": 1722226615.219, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722226612.9917, "finish": 1722226615.219, "ip": "", "conv_id": "bc62a47243314204a36eef314aba7158", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "Language models fine-tuning for non-language tasks without architectural changes", "output": [["Language models fine-tuning for non-language tasks without architectural changes", "Title: ClusterFace: Joint Clustering and Classification for Set-Based Face Recognition\n\nAbstract: Deep learning technology has enabled successful modeling of complex facial features when high quality images are available. Nonetheless, accurate modeling and recognition of human faces in real world scenarios `on the wild' or under adverse conditions remains an open problem. When unconstrained faces are mapped into deep features, variations such as illumination, pose, occlusion, etc., can create inconsistencies in the resultant feature space. Hence, deriving conclusions based on direct associations could lead to degraded performance. This rises the requirement for a basic feature space analysis prior to face recognition. This paper devises a joint clustering and classification scheme which learns deep face associations in an easy-to-hard way. Our method is based on hierarchical clustering where the early iterations tend to preserve high reliability. The rationale of our method is that a reliable clustering result can provide insights on the distribution of the feature space, that can guide the classification that follows. Experimental evaluations on three tasks, face verification, face identification and rank-order search, demonstrates better or competitive performance compared to the state-of-the-art, on all three experiments."]], "corpus": "arxiv"}
 {"tstamp": 1722226658.1901, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722226657.5319, "finish": 1722226658.1901, "ip": "", "conv_id": "bf820c0aa0c64ff9847080afc0f4d484", "model_name": "text-embedding-004", "prompt": "Research on improving the performance and energy efficiency of neural networks by utilizing 8-bit precision.", "output": [["Research on improving the performance and energy efficiency of neural networks by utilizing 8-bit precision.", "Title: Understanding the Impact of Precision Quantization on the Accuracy and Energy of Neural Networks\n\nAbstract: Deep neural networks are gaining in popularity as they are used to generate state-of-the-art results for a variety of computer vision and machine learning applications. At the same time, these networks have grown in depth and complexity in order to solve harder problems. Given the limitations in power budgets dedicated to these networks, the importance of low-power, low-memory solutions has been stressed in recent years. While a large number of dedicated hardware using different precisions has recently been proposed, there exists no comprehensive study of different bit precisions and arithmetic in both inputs and network parameters. In this work, we address this issue and perform a study of different bit-precisions in neural networks (from floating-point to fixed-point, powers of two, and binary). In our evaluation, we consider and analyze the effect of precision scaling on both network accuracy and hardware metrics including memory footprint, power and energy consumption, and design area. We also investigate training-time methodologies to compensate for the reduction in accuracy due to limited bit precision and demonstrate that in most cases, precision scaling can deliver significant benefits in design metrics at the cost of very modest decreases in network accuracy. In addition, we propose that a small portion of the benefits achieved when using lower precisions can be forfeited to increase the network size and therefore the accuracy. We evaluate our experiments, using three well-recognized networks and datasets to show its generality. We investigate the trade-offs and highlight the benefits of using lower precisions in terms of energy and memory footprint."]], "corpus": "arxiv"}
 {"tstamp": 1722226658.1901, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722226657.5319, "finish": 1722226658.1901, "ip": "", "conv_id": "8c0c14dc0aa642e382ed81777e5c8042", "model_name": "BM25", "prompt": "Research on improving the performance and energy efficiency of neural networks by utilizing 8-bit precision.", "output": [["Research on improving the performance and energy efficiency of neural networks by utilizing 8-bit precision.", "Title: Tartan: Accelerating Fully-Connected and Convolutional Layers in Deep Learning Networks by Exploiting Numerical Precision Variability\n\nAbstract: Tartan (TRT), a hardware accelerator for inference with Deep Neural Networks (DNNs), is presented and evaluated on Convolutional Neural Networks. TRT exploits the variable per layer precision requirements of DNNs to deliver execution time that is proportional to the precision p in bits used per layer for convolutional and fully-connected layers. Prior art has demonstrated an accelerator with the same execution performance only for convolutional layers. Experiments on image classification CNNs show that on average across all networks studied, TRT outperforms a state-of-the-art bit-parallel accelerator by 1:90x without any loss in accuracy while it is 1:17x more energy efficient. TRT requires no network retraining while it enables trading off accuracy for additional improvements in execution performance and energy efficiency. For example, if a 1% relative loss in accuracy is acceptable, TRT is on average 2:04x faster and 1:25x more energy efficient than a conventional bit-parallel accelerator. A Tartan configuration that processes 2-bits at time, requires less area than the 1-bit configuration, improves efficiency to 1:24x over the bit-parallel baseline while being 73% faster for convolutional layers and 60% faster for fully-connected layers is also presented."]], "corpus": "arxiv"}
@@ -101,10 +98,8 @@
 {"tstamp": 1722226850.0107, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722226849.9077, "finish": 1722226850.0107, "ip": "", "conv_id": "3b181c53b714491a82ac48e1a1950309", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "How do different constraints in storytelling tasks impact the author's linguistic style?", "output": [["How do different constraints in storytelling tasks impact the author's linguistic style?", "Title: The Effect of Different Writing Tasks on Linguistic Style: A Case Study of the ROC Story Cloze Task\n\nAbstract: A writer's style depends not just on personal traits but also on her intent and mental state. In this paper, we show how variants of the same writing task can lead to measurable differences in writing style. We present a case study based on the story cloze task (Mostafazadeh et al., 2016a), where annotators were assigned similar writing tasks with different constraints: (1) writing an entire story, (2) adding a story ending for a given story context, and (3) adding an incoherent ending to a story. We show that a simple linear classifier informed by stylistic features is able to successfully distinguish among the three cases, without even looking at the story context. In addition, combining our stylistic features with language model predictions reaches state of the art performance on the story cloze challenge. Our results demonstrate that different task framings can dramatically affect the way people write."]], "corpus": "arxiv"}
 {"tstamp": 1722226850.0107, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722226849.9077, "finish": 1722226850.0107, "ip": "", "conv_id": "de6bb332c59b4774b8c38bdad9af80a0", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "How do different constraints in storytelling tasks impact the author's linguistic style?", "output": [["How do different constraints in storytelling tasks impact the author's linguistic style?", "Title: Limits on dynamically generated spin-orbit coupling: Absence of $l=1$ Pomeranchuk instabilities in metals\n\nAbstract: An ordered state in the spin sector that breaks parity without breaking time-reversal symmetry, i.e., that can be considered as dynamically generated spin-orbit coupling, was proposed to explain puzzling observations in a range of different systems. Here we derive severe restrictions for such a state that follow from a Ward identity related to spin conservation. It is shown that $l=1$ spin-Pomeranchuk instabilities are not possible in non-relativistic systems since the response of spin-current fluctuations is entirely incoherent and non-singular. This rules out relativistic spin-orbit coupling as an emergent low-energy phenomenon. We illustrate the exotic physical properties of the remaining higher angular momentum analogues of spin-orbit coupling and derive a geometric constraint for spin-orbit vectors in lattice systems."]], "corpus": "arxiv"}
 {"tstamp": 1722226871.6124, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722226871.4532, "finish": 1722226871.6124, "ip": "", "conv_id": "85881b7aeaa44439a7c415dcfd68c525", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "Study on reducing gender bias in word-level models, showing improved results in bias evaluation metrics.", "output": [["Study on reducing gender bias in word-level models, showing improved results in bias evaluation metrics.", "Title: Chaos or Noise - Difficulties of a Distinction\n\nAbstract: In experiments, the dynamical behavior of systems is reflected in time series. Due to the finiteness of the observational data set it is not possible to reconstruct the invariant measure up to arbitrary fine resolution and arbitrary high embedding dimension. These restrictions limit our ability to distinguish between signals generated by different systems, such as regular, chaotic or stochastic ones, when analyzed from a time series point of view. We propose to classify the signal behavior, without referring to any specific model, as stochastic or deterministic on a certain scale of the resolution $\\epsilon$, according to the dependence of the $(\\epsilon,\\tau)$-entropy, $h(\\epsilon, \\tau)$, and of the finite size Lyapunov exponent, $\\lambda(\\epsilon)$, on $\\epsilon$."]], "corpus": "arxiv"}
-{"tstamp": 1722226871.6124, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722226871.4532, "finish": 1722226871.6124, "ip": "", "conv_id": "a7b2c40c9e5c43e3a3e400d04100c725", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "Study on reducing gender bias in word-level models, showing improved results in bias evaluation metrics.", "output": [["Study on reducing gender bias in word-level models, showing improved results in bias evaluation metrics.", "Title: Reducing Gender Bias in Word-Level Language Models with a Gender-Equalizing Loss Function\n\nAbstract: Gender bias exists in natural language datasets which neural language models tend to learn, resulting in biased text generation. In this research, we propose a debiasing approach based on the loss function modification. We introduce a new term to the loss function which attempts to equalize the probabilities of male and female words in the output. Using an array of bias evaluation metrics, we provide empirical evidence that our approach successfully mitigates gender bias in language models without increasing perplexity. In comparison to existing debiasing strategies, data augmentation, and word embedding debiasing, our method performs better in several aspects, especially in reducing gender bias in occupation words. Finally, we introduce a combination of data augmentation and our approach, and show that it outperforms existing strategies in all bias evaluation metrics."]], "corpus": "arxiv"}
 {"tstamp": 1722226883.5164, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722226883.3755, "finish": 1722226883.5164, "ip": "", "conv_id": "4987ca9238374025ae9f6d61145d0142", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "Study analyzing the multitask accuracy of text models including GPT-3 on academic and professional knowledge areas.", "output": [["Study analyzing the multitask accuracy of text models including GPT-3 on academic and professional knowledge areas.", "Title: Vibrational Spectra of Defects in Silicon: An Orbital Radii Approach\n\nAbstract: A phenomenological approach to the stretching mode vibrational frequencies of defects in semiconductors is proposed. A novel quantum scale is defined in terms of the first principles pseudopotential based orbital radius and the principal quantum number of the element concerned. A universal linear relationship between the Sanderson electronegativity and this quantum scale is established. Next, we show that the stretching mode vibrational frequencies of hydrogen and chlorine in the silicon network scale linearly with this quantum scale. Predictions and identifications of defect environments around the Si-H and Si-Cl are possible. The assignments of vibrational modes in porous silicon are critically examined. We discuss our proposed scale in the context of Mendeleveyan scales in general, and suggest justifications for it. We believe that our approach can be gainfully extended to the vibrational spectra of other semiconductors."]], "corpus": "arxiv"}
 {"tstamp": 1722226883.5164, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722226883.3755, "finish": 1722226883.5164, "ip": "", "conv_id": "bf81fa11eb3f4d3cb9c7294f31d17a63", "model_name": "GritLM/GritLM-7B", "prompt": "Study analyzing the multitask accuracy of text models including GPT-3 on academic and professional knowledge areas.", "output": [["Study analyzing the multitask accuracy of text models including GPT-3 on academic and professional knowledge areas.", "Title: Measuring Massive Multitask Language Understanding\n\nAbstract: We propose a new test to measure a text model's multitask accuracy. The test covers 57 tasks including elementary mathematics, US history, computer science, law, and more. To attain high accuracy on this test, models must possess extensive world knowledge and problem solving ability. We find that while most recent models have near random-chance accuracy, the very largest GPT-3 model improves over random chance by almost 20 percentage points on average. However, on every one of the 57 tasks, the best models still need substantial improvements before they can reach expert-level accuracy. Models also have lopsided performance and frequently do not know when they are wrong. Worse, they still have near-random accuracy on some socially important subjects such as morality and law. By comprehensively evaluating the breadth and depth of a model's academic and professional understanding, our test can be used to analyze models across many tasks and to identify important shortcomings."]], "corpus": "arxiv"}
-{"tstamp": 1722226897.3303, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722226897.1552, "finish": 1722226897.3303, "ip": "", "conv_id": "35ba5141430e439182b4cb93495f60b2", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "Comparison of sparse attention and hierarchical encoding in long document transformers", "output": [["Comparison of sparse attention and hierarchical encoding in long document transformers", "Title: Revisiting Transformer-based Models for Long Document Classification\n\nAbstract: The recent literature in text classification is biased towards short text sequences (e.g., sentences or paragraphs). In real-world applications, multi-page multi-paragraph documents are common and they cannot be efficiently encoded by vanilla Transformer-based models. We compare different Transformer-based Long Document Classification (TrLDC) approaches that aim to mitigate the computational overhead of vanilla transformers to encode much longer text, namely sparse attention and hierarchical encoding methods. We examine several aspects of sparse attention (e.g., size of local attention window, use of global attention) and hierarchical (e.g., document splitting strategy) transformers on four document classification datasets covering different domains. We observe a clear benefit from being able to process longer text, and, based on our results, we derive practical advice of applying Transformer-based models on long document classification tasks."]], "corpus": "arxiv"}
 {"tstamp": 1722226897.3303, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722226897.1552, "finish": 1722226897.3303, "ip": "", "conv_id": "7c53ae4004484956a4ab85f088046698", "model_name": "GritLM/GritLM-7B", "prompt": "Comparison of sparse attention and hierarchical encoding in long document transformers", "output": [["Comparison of sparse attention and hierarchical encoding in long document transformers", "Title: Revisiting Transformer-based Models for Long Document Classification\n\nAbstract: The recent literature in text classification is biased towards short text sequences (e.g., sentences or paragraphs). In real-world applications, multi-page multi-paragraph documents are common and they cannot be efficiently encoded by vanilla Transformer-based models. We compare different Transformer-based Long Document Classification (TrLDC) approaches that aim to mitigate the computational overhead of vanilla transformers to encode much longer text, namely sparse attention and hierarchical encoding methods. We examine several aspects of sparse attention (e.g., size of local attention window, use of global attention) and hierarchical (e.g., document splitting strategy) transformers on four document classification datasets covering different domains. We observe a clear benefit from being able to process longer text, and, based on our results, we derive practical advice of applying Transformer-based models on long document classification tasks."]], "corpus": "arxiv"}
 {"tstamp": 1722226909.7935, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722226909.6981, "finish": 1722226909.7935, "ip": "", "conv_id": "565cd683cdf7445296d0b3a2377c66c4", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "Unsupervised Data Generation technique for creating training data to train NLP models without annotations.", "output": [["Unsupervised Data Generation technique for creating training data to train NLP models without annotations.", "Title: Towards Zero-Label Language Learning\n\nAbstract: This paper explores zero-label learning in Natural Language Processing (NLP), whereby no human-annotated data is used anywhere during training and models are trained purely on synthetic data. At the core of our framework is a novel approach for better leveraging the powerful pretrained language models. Specifically, inspired by the recent success of few-shot inference on GPT-3, we present a training data creation procedure named Unsupervised Data Generation (UDG), which leverages few-shot prompts to synthesize high-quality training data without real human annotations. Our method enables zero-label learning as we train task-specific models solely on the synthetic data, yet we achieve better or comparable results from strong baseline models trained on human-labeled data. Furthermore, when mixed with labeled data, our approach serves as a highly effective data augmentation procedure, achieving new state-of-the-art results on the SuperGLUE benchmark."]], "corpus": "arxiv"}
 {"tstamp": 1722226909.7935, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722226909.6981, "finish": 1722226909.7935, "ip": "", "conv_id": "8bf31665ddaf4843a18cb2798d0df7e0", "model_name": "BM25", "prompt": "Unsupervised Data Generation technique for creating training data to train NLP models without annotations.", "output": [["Unsupervised Data Generation technique for creating training data to train NLP models without annotations.", "Title: Healthcare NER Models Using Language Model Pretraining\n\nAbstract: In this paper, we present our approach to extracting structured information from unstructured Electronic Health Records (EHR) [2] which can be used to, for example, study adverse drug reactions in patients due to chemicals in their products. Our solution uses a combination of Natural Language Processing (NLP) techniques and a web-based annotation tool to optimize the performance of a custom Named Entity Recognition (NER) [1] model trained on a limited amount of EHR training data. This work was presented at the first Health Search and Data Mining Workshop (HSDM 2020) [26]. We showcase a combination of tools and techniques leveraging the recent advancements in NLP aimed at targeting domain shifts by applying transfer learning and language model pre-training techniques [3]. We present a comparison of our technique to the current popular approaches and show the effective increase in performance of the NER model and the reduction in time to annotate data.A key observation of the results presented is that the F1 score of model (0.734) trained with our approach with just 50% of available training data outperforms the F1 score of the blank spaCy model without language model component (0.704) trained with 100% of the available training data. We also demonstrate an annotation tool to minimize domain expert time and the manual effort required to generate such a training dataset. Further, we plan to release the annotated dataset as well as the pre-trained model to the community to further research in medical health records."]], "corpus": "arxiv"}
@@ -112,7 +107,6 @@
 {"tstamp": 1722226963.5592, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722226963.4173, "finish": 1722226963.5592, "ip": "", "conv_id": "6ac5d103c1db4b2ca3c16a5eaf7fb9dd", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "Research about speech enhancement with speaker-aware features and no auxiliary test-phase signals", "output": [["Research about speech enhancement with speaker-aware features and no auxiliary test-phase signals", "Title: Speech Enhancement using Self-Adaptation and Multi-Head Self-Attention\n\nAbstract: This paper investigates a self-adaptation method for speech enhancement using auxiliary speaker-aware features; we extract a speaker representation used for adaptation directly from the test utterance. Conventional studies of deep neural network (DNN)--based speech enhancement mainly focus on building a speaker independent model. Meanwhile, in speech applications including speech recognition and synthesis, it is known that model adaptation to the target speaker improves the accuracy. Our research question is whether a DNN for speech enhancement can be adopted to unknown speakers without any auxiliary guidance signal in test-phase. To achieve this, we adopt multi-task learning of speech enhancement and speaker identification, and use the output of the final hidden layer of speaker identification branch as an auxiliary feature. In addition, we use multi-head self-attention for capturing long-term dependencies in the speech and noise. Experimental results on a public dataset show that our strategy achieves the state-of-the-art performance and also outperform conventional methods in terms of subjective quality."]], "corpus": "arxiv"}
 {"tstamp": 1722226979.3012, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722226979.1956, "finish": 1722226979.3012, "ip": "", "conv_id": "c81179613507466f9501f236a8beb4a9", "model_name": "BM25", "prompt": "Paper introducing universal adversarial input triggers to analyze vulnerabilities in NLP models and their transferability across different models.", "output": [["Paper introducing universal adversarial input triggers to analyze vulnerabilities in NLP models and their transferability across different models.", "Title: Universal Adversarial Triggers for Attacking and Analyzing NLP\n\nAbstract: Adversarial examples highlight model vulnerabilities and are useful for evaluation and interpretation. We define universal adversarial triggers: input-agnostic sequences of tokens that trigger a model to produce a specific prediction when concatenated to any input from a dataset. We propose a gradient-guided search over tokens which finds short trigger sequences (e.g., one word for classification and four words for language modeling) that successfully trigger the target prediction. For example, triggers cause SNLI entailment accuracy to drop from 89.94% to 0.55%, 72% of \"why\" questions in SQuAD to be answered \"to kill american people\", and the GPT-2 language model to spew racist output even when conditioned on non-racial contexts. Furthermore, although the triggers are optimized using white-box access to a specific model, they transfer to other models for all tasks we consider. Finally, since triggers are input-agnostic, they provide an analysis of global model behavior. For instance, they confirm that SNLI models exploit dataset biases and help to diagnose heuristics learned by reading comprehension models."]], "corpus": "arxiv"}
 {"tstamp": 1722226979.3012, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722226979.1956, "finish": 1722226979.3012, "ip": "", "conv_id": "95105e1e8df945a7b819bd50bcd0a76a", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "Paper introducing universal adversarial input triggers to analyze vulnerabilities in NLP models and their transferability across different models.", "output": [["Paper introducing universal adversarial input triggers to analyze vulnerabilities in NLP models and their transferability across different models.", "Title: Universal Adversarial Triggers for Attacking and Analyzing NLP\n\nAbstract: Adversarial examples highlight model vulnerabilities and are useful for evaluation and interpretation. We define universal adversarial triggers: input-agnostic sequences of tokens that trigger a model to produce a specific prediction when concatenated to any input from a dataset. We propose a gradient-guided search over tokens which finds short trigger sequences (e.g., one word for classification and four words for language modeling) that successfully trigger the target prediction. For example, triggers cause SNLI entailment accuracy to drop from 89.94% to 0.55%, 72% of \"why\" questions in SQuAD to be answered \"to kill american people\", and the GPT-2 language model to spew racist output even when conditioned on non-racial contexts. Furthermore, although the triggers are optimized using white-box access to a specific model, they transfer to other models for all tasks we consider. Finally, since triggers are input-agnostic, they provide an analysis of global model behavior. For instance, they confirm that SNLI models exploit dataset biases and help to diagnose heuristics learned by reading comprehension models."]], "corpus": "arxiv"}
-{"tstamp": 1722226991.5645, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722226991.3804, "finish": 1722226991.5645, "ip": "", "conv_id": "c966ef1f66124006834ce9fae7ec6c57", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "Adversarial example generation for text classification using BERT masked language model  ", "output": [["Adversarial example generation for text classification using BERT masked language model  ", "Title: Measuring Adversarial Datasets\n\nAbstract: In the era of widespread public use of AI systems across various domains, ensuring adversarial robustness has become increasingly vital to maintain safety and prevent undesirable errors. Researchers have curated various adversarial datasets (through perturbations) for capturing model deficiencies that cannot be revealed in standard benchmark datasets. However, little is known about how these adversarial examples differ from the original data points, and there is still no methodology to measure the intended and unintended consequences of those adversarial transformations. In this research, we conducted a systematic survey of existing quantifiable metrics that describe text instances in NLP tasks, among dimensions of difficulty, diversity, and disagreement. We selected several current adversarial effect datasets and compared the distributions between the original and their adversarial counterparts. The results provide valuable insights into what makes these datasets more challenging from a metrics perspective and whether they align with underlying assumptions."]], "corpus": "arxiv"}
 {"tstamp": 1722227001.4038, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722227001.3034, "finish": 1722227001.4038, "ip": "", "conv_id": "73fda287fa15475a92a663d6a3dba7cb", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "Achieving high accuracy in English-French plagiarism detection using word embeddings", "output": [["Achieving high accuracy in English-French plagiarism detection using word embeddings", "Title: UsingWord Embedding for Cross-Language Plagiarism Detection\n\nAbstract: This paper proposes to use distributed representation of words (word embeddings) in cross-language textual similarity detection. The main contributions of this paper are the following: (a) we introduce new cross-language similarity detection methods based on distributed representation of words; (b) we combine the different methods proposed to verify their complementarity and finally obtain an overall F1 score of 89.15% for English-French similarity detection at chunk level (88.5% at sentence level) on a very challenging corpus."]], "corpus": "arxiv"}
 {"tstamp": 1722227001.4038, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722227001.3034, "finish": 1722227001.4038, "ip": "", "conv_id": "339520347d484e1c8068e44e4e4e7452", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "Achieving high accuracy in English-French plagiarism detection using word embeddings", "output": [["Achieving high accuracy in English-French plagiarism detection using word embeddings", "Title: Studies of Plasma Detachment Using a One Dimensional Model for Divertor Operation\n\nAbstract: To characterize the conditions required to reach advanced divertor regimes, a one-dimensional computational model has been developed based on a coordinate transformation to incorporate two-dimensional effects. This model includes transport of ions, two species each of atoms and molecules, momentum, and ion and electron energy both within and across the flux surfaces. Impurity radiation is calculated using a coronal equilibrium model which includes the effects of charge-exchange recombination. Numerical results indicate that impurity radiation acts to facilitate plasma detachment and enhances the power lost from the divertor channel in escaping neutral atoms by cooling the electrons and suppressing ionization. As divertor particle densities increase, cold and thermal molecules become increasingly important in cooling the plasma, with molecular densities dominating electron and atomic densities under some conditions."]], "corpus": "arxiv"}
 {"tstamp": 1722227013.155, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722227012.9814, "finish": 1722227013.155, "ip": "", "conv_id": "de8d07d9dc434154b214bde3478b3319", "model_name": "BM25", "prompt": "A paper presenting a Transformer model for vision-and-language tasks that does not rely on object detection or ResNet.", "output": [["A paper presenting a Transformer model for vision-and-language tasks that does not rely on object detection or ResNet.", "Title: ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision\n\nAbstract: Vision-and-Language Pre-training (VLP) has improved performance on various joint vision-and-language downstream tasks. Current approaches to VLP heavily rely on image feature extraction processes, most of which involve region supervision (e.g., object detection) and the convolutional architecture (e.g., ResNet). Although disregarded in the literature, we find it problematic in terms of both (1) efficiency/speed, that simply extracting input features requires much more computation than the multimodal interaction steps; and (2) expressive power, as it is upper bounded to the expressive power of the visual embedder and its predefined visual vocabulary. In this paper, we present a minimal VLP model, Vision-and-Language Transformer (ViLT), monolithic in the sense that the processing of visual inputs is drastically simplified to just the same convolution-free manner that we process textual inputs. We show that ViLT is up to tens of times faster than previous VLP models, yet with competitive or better downstream task performance. Our code and pre-trained weights are available at https://github.com/dandelin/vilt."]], "corpus": "arxiv"}
@@ -121,7 +115,6 @@
 {"tstamp": 1722227020.2398, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722227019.6047, "finish": 1722227020.2398, "ip": "", "conv_id": "f0f4c01bfc3f47babe5c73dfa7135da2", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "How do explanations in context influence the performance of language models?", "output": [["How do explanations in context influence the performance of language models?", "Title: Can language models learn from explanations in context?\n\nAbstract: Language Models (LMs) can perform new tasks by adapting to a few in-context examples. For humans, explanations that connect examples to task principles can improve learning. We therefore investigate whether explanations of few-shot examples can help LMs. We annotate questions from 40 challenging tasks with answer explanations, and various matched control explanations. We evaluate how different types of explanations, instructions, and controls affect zero- and few-shot performance. We analyze these results using statistical multilevel modeling techniques that account for the nested dependencies among conditions, tasks, prompts, and models. We find that explanations can improve performance -- even without tuning. Furthermore, explanations hand-tuned for performance on a small validation set offer substantially larger benefits, and building a prompt by selecting examples and explanations together substantially improves performance over selecting examples alone. Finally, even untuned explanations outperform carefully matched controls, suggesting that the benefits are due to the link between an example and its explanation, rather than lower-level features. However, only large models benefit. In summary, explanations can support the in-context learning of large LMs on challenging tasks."]], "corpus": "arxiv"}
 {"tstamp": 1722227026.6265, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722227026.1188, "finish": 1722227026.6265, "ip": "", "conv_id": "ce9a309e5ad24ba38deee7ff08db73eb", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "Study introducing a BERT-based framework achieving superior performance on scalable DST benchmarks like Sim-M and Sim-R.", "output": [["Study introducing a BERT-based framework achieving superior performance on scalable DST benchmarks like Sim-M and Sim-R.", "Title: BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformer\n\nAbstract: An important yet rarely tackled problem in dialogue state tracking (DST) is scalability for dynamic ontology (e.g., movie, restaurant) and unseen slot values. We focus on a specific condition, where the ontology is unknown to the state tracker, but the target slot value (except for none and dontcare), possibly unseen during training, can be found as word segment in the dialogue context. Prior approaches often rely on candidate generation from n-gram enumeration or slot tagger outputs, which can be inefficient or suffer from error propagation. We propose BERT-DST, an end-to-end dialogue state tracker which directly extracts slot values from the dialogue context. We use BERT as dialogue context encoder whose contextualized language representations are suitable for scalable DST to identify slot values from their semantic context. Furthermore, we employ encoder parameter sharing across all slots with two advantages: (1) Number of parameters does not grow linearly with the ontology. (2) Language representation knowledge can be transferred among slots. Empirical evaluation shows BERT-DST with cross-slot parameter sharing outperforms prior work on the benchmark scalable DST datasets Sim-M and Sim-R, and achieves competitive performance on the standard DSTC2 and WOZ 2.0 datasets."]], "corpus": "arxiv"}
 {"tstamp": 1722227026.6265, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722227026.1188, "finish": 1722227026.6265, "ip": "", "conv_id": "fa33616b16934385bfd92cd0a0c413cf", "model_name": "text-embedding-3-large", "prompt": "Study introducing a BERT-based framework achieving superior performance on scalable DST benchmarks like Sim-M and Sim-R.", "output": [["Study introducing a BERT-based framework achieving superior performance on scalable DST benchmarks like Sim-M and Sim-R.", "Title: BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformer\n\nAbstract: An important yet rarely tackled problem in dialogue state tracking (DST) is scalability for dynamic ontology (e.g., movie, restaurant) and unseen slot values. We focus on a specific condition, where the ontology is unknown to the state tracker, but the target slot value (except for none and dontcare), possibly unseen during training, can be found as word segment in the dialogue context. Prior approaches often rely on candidate generation from n-gram enumeration or slot tagger outputs, which can be inefficient or suffer from error propagation. We propose BERT-DST, an end-to-end dialogue state tracker which directly extracts slot values from the dialogue context. We use BERT as dialogue context encoder whose contextualized language representations are suitable for scalable DST to identify slot values from their semantic context. Furthermore, we employ encoder parameter sharing across all slots with two advantages: (1) Number of parameters does not grow linearly with the ontology. (2) Language representation knowledge can be transferred among slots. Empirical evaluation shows BERT-DST with cross-slot parameter sharing outperforms prior work on the benchmark scalable DST datasets Sim-M and Sim-R, and achieves competitive performance on the standard DSTC2 and WOZ 2.0 datasets."]], "corpus": "arxiv"}
-{"tstamp": 1722227036.8182, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722227036.6406, "finish": 1722227036.8182, "ip": "", "conv_id": "d4f40214f39349929660960ef995c744", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "A study showing the drawbacks of using standard MLE training and offering a new training procedure for better test performance in language models.", "output": [["A study showing the drawbacks of using standard MLE training and offering a new training procedure for better test performance in language models.", "Title: Recurrent Neural Language Models as Probabilistic Finite-state Automata\n\nAbstract: Studying language models (LMs) in terms of well-understood formalisms allows us to precisely characterize their abilities and limitations. Previous work has investigated the representational capacity of recurrent neural network (RNN) LMs in terms of their capacity to recognize unweighted formal languages. However, LMs do not describe unweighted formal languages -- rather, they define \\emph{probability distributions} over strings. In this work, we study what classes of such probability distributions RNN LMs can represent, which allows us to make more direct statements about their capabilities. We show that simple RNNs are equivalent to a subclass of probabilistic finite-state automata, and can thus model a strict subset of probability distributions expressible by finite-state models. Furthermore, we study the space complexity of representing finite-state LMs with RNNs. We show that, to represent an arbitrary deterministic finite-state LM with $N$ states over an alphabet $\\alphabet$, an RNN requires $\\Omega\\left(N |\\Sigma|\\right)$ neurons. These results present a first step towards characterizing the classes of distributions RNN LMs can represent and thus help us understand their capabilities and limitations."]], "corpus": "arxiv"}
 {"tstamp": 1722227036.8182, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722227036.6406, "finish": 1722227036.8182, "ip": "", "conv_id": "d39dbe79ef5d443683896e332508c895", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "A study showing the drawbacks of using standard MLE training and offering a new training procedure for better test performance in language models.", "output": [["A study showing the drawbacks of using standard MLE training and offering a new training procedure for better test performance in language models.", "Title: Neural Architecture Search as Sparse Supernet\n\nAbstract: This paper aims at enlarging the problem of Neural Architecture Search (NAS) from Single-Path and Multi-Path Search to automated Mixed-Path Search. In particular, we model the NAS problem as a sparse supernet using a new continuous architecture representation with a mixture of sparsity constraints. The sparse supernet enables us to automatically achieve sparsely-mixed paths upon a compact set of nodes. To optimize the proposed sparse supernet, we exploit a hierarchical accelerated proximal gradient algorithm within a bi-level optimization framework. Extensive experiments on Convolutional Neural Network and Recurrent Neural Network search demonstrate that the proposed method is capable of searching for compact, general and powerful neural architectures."]], "corpus": "arxiv"}
 {"tstamp": 1722227070.1836, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722227070.0115, "finish": 1722227070.1836, "ip": "", "conv_id": "ec5eb017dc4d4d9fa6d04d114fcc2e00", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "Propaganda-as-a-service via training-time attacks on language models for biased text generation  ", "output": [["Propaganda-as-a-service via training-time attacks on language models for biased text generation  ", "Title: Spinning Language Models: Risks of Propaganda-As-A-Service and Countermeasures\n\nAbstract: We investigate a new threat to neural sequence-to-sequence (seq2seq) models: training-time attacks that cause models to \"spin\" their outputs so as to support an adversary-chosen sentiment or point of view -- but only when the input contains adversary-chosen trigger words. For example, a spinned summarization model outputs positive summaries of any text that mentions the name of some individual or organization. Model spinning introduces a \"meta-backdoor\" into a model. Whereas conventional backdoors cause models to produce incorrect outputs on inputs with the trigger, outputs of spinned models preserve context and maintain standard accuracy metrics, yet also satisfy a meta-task chosen by the adversary. Model spinning enables propaganda-as-a-service, where propaganda is defined as biased speech. An adversary can create customized language models that produce desired spins for chosen triggers, then deploy these models to generate disinformation (a platform attack), or else inject them into ML training pipelines (a supply-chain attack), transferring malicious functionality to downstream models trained by victims. To demonstrate the feasibility of model spinning, we develop a new backdooring technique. It stacks an adversarial meta-task onto a seq2seq model, backpropagates the desired meta-task output to points in the word-embedding space we call \"pseudo-words,\" and uses pseudo-words to shift the entire output distribution of the seq2seq model. We evaluate this attack on language generation, summarization, and translation models with different triggers and meta-tasks such as sentiment, toxicity, and entailment. Spinned models largely maintain their accuracy metrics (ROUGE and BLEU) while shifting their outputs to satisfy the adversary's meta-task. We also show that, in the case of a supply-chain attack, the spin functionality transfers to downstream models."]], "corpus": "arxiv"}
 {"tstamp": 1722227070.1836, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722227070.0115, "finish": 1722227070.1836, "ip": "", "conv_id": "009892afcd5f438aa105fea295c61e62", "model_name": "GritLM/GritLM-7B", "prompt": "Propaganda-as-a-service via training-time attacks on language models for biased text generation  ", "output": [["Propaganda-as-a-service via training-time attacks on language models for biased text generation  ", "Title: Spinning Language Models: Risks of Propaganda-As-A-Service and Countermeasures\n\nAbstract: We investigate a new threat to neural sequence-to-sequence (seq2seq) models: training-time attacks that cause models to \"spin\" their outputs so as to support an adversary-chosen sentiment or point of view -- but only when the input contains adversary-chosen trigger words. For example, a spinned summarization model outputs positive summaries of any text that mentions the name of some individual or organization. Model spinning introduces a \"meta-backdoor\" into a model. Whereas conventional backdoors cause models to produce incorrect outputs on inputs with the trigger, outputs of spinned models preserve context and maintain standard accuracy metrics, yet also satisfy a meta-task chosen by the adversary. Model spinning enables propaganda-as-a-service, where propaganda is defined as biased speech. An adversary can create customized language models that produce desired spins for chosen triggers, then deploy these models to generate disinformation (a platform attack), or else inject them into ML training pipelines (a supply-chain attack), transferring malicious functionality to downstream models trained by victims. To demonstrate the feasibility of model spinning, we develop a new backdooring technique. It stacks an adversarial meta-task onto a seq2seq model, backpropagates the desired meta-task output to points in the word-embedding space we call \"pseudo-words,\" and uses pseudo-words to shift the entire output distribution of the seq2seq model. We evaluate this attack on language generation, summarization, and translation models with different triggers and meta-tasks such as sentiment, toxicity, and entailment. Spinned models largely maintain their accuracy metrics (ROUGE and BLEU) while shifting their outputs to satisfy the adversary's meta-task. We also show that, in the case of a supply-chain attack, the spin functionality transfers to downstream models."]], "corpus": "arxiv"}
@@ -140,7 +133,6 @@
 {"tstamp": 1722227160.6028, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722227160.5002, "finish": 1722227160.6028, "ip": "", "conv_id": "2f3d197a65dc467ca5bfd7baa9795464", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "A model improving question generation and answering with Transformers in semi-supervised setup.", "output": [["A model improving question generation and answering with Transformers in semi-supervised setup.", "Title: Machine Comprehension by Text-to-Text Neural Question Generation\n\nAbstract: We propose a recurrent neural model that generates natural-language questions from documents, conditioned on answers. We show how to train the model using a combination of supervised and reinforcement learning. After teacher forcing for standard maximum likelihood training, we fine-tune the model using policy gradient techniques to maximize several rewards that measure question quality. Most notably, one of these rewards is the performance of a question-answering system. We motivate question generation as a means to improve the performance of question answering systems. Our model is trained and evaluated on the recent question-answering dataset SQuAD."]], "corpus": "arxiv"}
 {"tstamp": 1722227160.6028, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722227160.5002, "finish": 1722227160.6028, "ip": "", "conv_id": "6fe550931a3f4d108db6ae848b48f728", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "A model improving question generation and answering with Transformers in semi-supervised setup.", "output": [["A model improving question generation and answering with Transformers in semi-supervised setup.", "Title: Hexagons and Interfaces in a Vibrated Granular Layer\n\nAbstract: The order parameter model based on parametric Ginzburg-Landau equation is used to describe high acceleration patterns in vibrated layer of granular material. At large amplitude of driving both hexagons and interfaces emerge. Transverse instability leading to formation of ``decorated'' interfaces and labyrinthine patterns, is found. Additional sub-harmonic forcing leads to controlled interface motion."]], "corpus": "arxiv"}
 {"tstamp": 1722227173.3841, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722227173.2783, "finish": 1722227173.3841, "ip": "", "conv_id": "df087591632840268d7cda35b4eee3d6", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "A model replacing RNN/CNN with self-attention for better HAR results  ", "output": [["A model replacing RNN/CNN with self-attention for better HAR results  ", "Title: Partial Representations and Amenable Fell Bundles over Free Groups\n\nAbstract: We show that a Fell bundle B = {B_t}_{t \\in F}, over an arbitrary free group F, is amenable, whenever it is orthogonal (in the sense that B_x^* B_y = 0, if x and y are distinct generators of F) and semi-saturated (in the sense that B_{ts} coincides with the closed linear span of B_t B_s, when the multiplication ``ts'' involves no cancelation)."]], "corpus": "arxiv"}
-{"tstamp": 1722227198.6748, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722227198.3259, "finish": 1722227198.6748, "ip": "", "conv_id": "4ec2689a12834a6db1bff4000f809ac7", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "A comprehensive study on an autoregressive language model that achieves competitive results without fine-tuning on translation, question-answering, and other tasks.", "output": [["A comprehensive study on an autoregressive language model that achieves competitive results without fine-tuning on translation, question-answering, and other tasks.", "Title: A Comprehensive Performance Study of Large Language Models on Novel AI Accelerators\n\nAbstract: Artificial intelligence (AI) methods have become critical in scientific applications to help accelerate scientific discovery. Large language models (LLMs) are being considered as a promising approach to address some of the challenging problems because of their superior generalization capabilities across domains. The effectiveness of the models and the accuracy of the applications is contingent upon their efficient execution on the underlying hardware infrastructure. Specialized AI accelerator hardware systems have recently become available for accelerating AI applications. However, the comparative performance of these AI accelerators on large language models has not been previously studied. In this paper, we systematically study LLMs on multiple AI accelerators and GPUs and evaluate their performance characteristics for these models. We evaluate these systems with (i) a micro-benchmark using a core transformer block, (ii) a GPT- 2 model, and (iii) an LLM-driven science use case, GenSLM. We present our findings and analyses of the models' performance to better understand the intrinsic capabilities of AI accelerators. Furthermore, our analysis takes into account key factors such as sequence lengths, scaling behavior, sparsity, and sensitivity to gradient accumulation steps."]], "corpus": "arxiv"}
 {"tstamp": 1722227198.6748, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722227198.3259, "finish": 1722227198.6748, "ip": "", "conv_id": "fc6506362156431bbfcc4838c0170354", "model_name": "voyage-multilingual-2", "prompt": "A comprehensive study on an autoregressive language model that achieves competitive results without fine-tuning on translation, question-answering, and other tasks.", "output": [["A comprehensive study on an autoregressive language model that achieves competitive results without fine-tuning on translation, question-answering, and other tasks.", "Title: What Have We Achieved on Non-autoregressive Translation?\n\nAbstract: Recent advances have made non-autoregressive (NAT) translation comparable to autoregressive methods (AT). However, their evaluation using BLEU has been shown to weakly correlate with human annotations. Limited research compares non-autoregressive translation and autoregressive translation comprehensively, leaving uncertainty about the true proximity of NAT to AT. To address this gap, we systematically evaluate four representative NAT methods across various dimensions, including human evaluation. Our empirical results demonstrate that despite narrowing the performance gap, state-of-the-art NAT still underperforms AT under more reliable evaluation metrics. Furthermore, we discover that explicitly modeling dependencies is crucial for generating natural language and generalizing to out-of-distribution sequences."]], "corpus": "arxiv"}
 {"tstamp": 1722227223.7108, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722227223.0167, "finish": 1722227223.7108, "ip": "", "conv_id": "a94f5283b42849a2a94a8bea42b41dfa", "model_name": "GritLM/GritLM-7B", "prompt": "Seeking a paper on refining model predictions layer-by-layer in transformers with improved techniques.", "output": [["Seeking a paper on refining model predictions layer-by-layer in transformers with improved techniques.", "Title: Eliciting Latent Predictions from Transformers with the Tuned Lens\n\nAbstract: We analyze transformers from the perspective of iterative inference, seeking to understand how model predictions are refined layer by layer. To do so, we train an affine probe for each block in a frozen pretrained model, making it possible to decode every hidden state into a distribution over the vocabulary. Our method, the \\emph{tuned lens}, is a refinement of the earlier ``logit lens'' technique, which yielded useful insights but is often brittle. We test our method on various autoregressive language models with up to 20B parameters, showing it to be more predictive, reliable and unbiased than the logit lens. With causal experiments, we show the tuned lens uses similar features to the model itself. We also find the trajectory of latent predictions can be used to detect malicious inputs with high accuracy. All code needed to reproduce our results can be found at https://github.com/AlignmentResearch/tuned-lens."]], "corpus": "arxiv"}
 {"tstamp": 1722227223.7108, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722227223.0167, "finish": 1722227223.7108, "ip": "", "conv_id": "d68f11f0d93a4c03b842f212c55afb7f", "model_name": "text-embedding-004", "prompt": "Seeking a paper on refining model predictions layer-by-layer in transformers with improved techniques.", "output": [["Seeking a paper on refining model predictions layer-by-layer in transformers with improved techniques.", "Title: Rewiring the Transformer with Depth-Wise LSTMs\n\nAbstract: Stacking non-linear layers allows deep neural networks to model complicated functions, and including residual connections in Transformer layers is beneficial for convergence and performance. However, residual connections may make the model \"forget\" distant layers and fail to fuse information from previous layers effectively. Selectively managing the representation aggregation of Transformer layers may lead to better performance. In this paper, we present a Transformer with depth-wise LSTMs connecting cascading Transformer layers and sub-layers. We show that layer normalization and feed-forward computation within a Transformer layer can be absorbed into depth-wise LSTMs connecting pure Transformer attention layers. Our experiments with the 6-layer Transformer show significant BLEU improvements in both WMT 14 English-German / French tasks and the OPUS-100 many-to-many multilingual NMT task, and our deep Transformer experiments demonstrate the effectiveness of depth-wise LSTM on the convergence and performance of deep Transformers."]], "corpus": "arxiv"}

data/retrieval_side_by_side-407c4836-37e2-4f9f-8e9b-06706cc4440c.jsonl

@@ -1,14 +1,13 @@
 {"tstamp": 1722445866.7856, "task_type": "retrieval", "type": "leftvote", "models": ["nomic-ai/nomic-embed-text-v1.5", "jinaai/jina-embeddings-v2-base-en"], "ip": "", "0_conv_id": "44be4054380e46ddb723a2cb471d95ca", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": "Recent work on embedding text from multiple languages into a common semantic space using dual-encoder models", "0_output": [["Recent work on embedding text from multiple languages into a common semantic space using dual-encoder models", "Title: Multilingual Universal Sentence Encoder for Semantic Retrieval\n\nAbstract: We introduce two pre-trained retrieval focused multilingual sentence encoding models, respectively based on the Transformer and CNN model architectures. The models embed text from 16 languages into a single semantic space using a multi-task trained dual-encoder that learns tied representations using translation based bridge tasks (Chidambaram al., 2018). The models provide performance that is competitive with the state-of-the-art on: semantic retrieval (SR), translation pair bitext retrieval (BR) and retrieval question answering (ReQA). On English transfer learning tasks, our sentence-level embeddings approach, and in some cases exceed, the performance of monolingual, English only, sentence embedding models. Our models are made available for download on TensorFlow Hub."]], "0_corpus": "arxiv", "1_conv_id": "2ee560deb1bb478ba7cc802e6642343d", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_prompt": "Recent work on embedding text from multiple languages into a common semantic space using dual-encoder models", "1_output": [["Recent work on embedding text from multiple languages into a common semantic space using dual-encoder models", "Title: Point-charge electrostatics in disordered alloys\n\nAbstract: A simple analytic model of point-ion electrostatics has been previously proposed in which the magnitude of the net charge q_i on each atom in an ordered or random alloy depends linearly on the number N_i^(1) of unlike neighbors in its first coordination shell. Point charges extracted from recent large supercell (256-432 atom) local density approximation (LDA) calculations of Cu-Zn random alloys now enable an assessment of the physical validity and accuracy of the simple model. We find that this model accurately describes (i) the trends in q_i vs. N_i^(1), particularly for fcc alloys, (ii) the magnitudes of total electrostatic energies in random alloys, (iii) the relationships between constant-occupation-averaged charges <q_i> and Coulomb shifts <V_i> (i.e., the average over all sites occupied by either $A$ or $B$ atoms) in the random alloy, and (iv) the linear relation between the site charge q_i and the constant- charge-averaged Coulomb shift (i.e., the average over all sites with the same charge) for fcc alloys. However, for bcc alloys the fluctuations predicted by the model in the q_i vs. V_i relation exceed those found in the LDA supercell calculations. We find that (a) the fluctuations present in the model have a vanishing contribution to the electrostatic energy. (b) Generalizing the model to include a dependence of the charge on the atoms in the first three (two) shells in bcc (fcc) - rather than the first shell only - removes the fluctuations, in complete agreement with the LDA data. We also demonstrate an efficient way to extract charge transfer parameters of the generalized model from LDA calculations on small unit cells."]], "1_corpus": "arxiv"}
 {"tstamp": 1722445932.7055, "task_type": "retrieval", "type": "leftvote", "models": ["nomic-ai/nomic-embed-text-v1.5", "GritLM/GritLM-7B"], "ip": "", "0_conv_id": "b96ff4fc7f0443eeac98061a3dfffce4", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": "one flew over the cuckoo's nest why did chief pretend to be deaf?", "0_output": [["one flew over the cuckoo's nest why did chief pretend to be deaf?", "Scifi Stackexchange\n\nQ: What story was Jack telling when Elizabeth fell? What story was Jack telling when Elizabeth fell? Can someone provide the whole story?\n\n\nA: The scene was ad-libbed by Johnny Depp. It's a reference to The Fast Show (which Depp is a fan of), where Patrick Nice often says \"...and [something happened]...which was nice\". In one episode, Nice explicitly says this line:\n\n...and then they made me their chief... which was nice.\n\nThat said, the second movie: Dead Man's Chest involves Sparrow as the chief for the Pelegostos Tribe. According to the DVD audio commentary with Ted Elliott and Terry Rossio, this is the second time that he has visited them, with the first being the one that was referenced by this line.\nThe full story of his initial adventure with the tribe was never explained. TV Tropes would call this a noodle incident.\n"]], "0_corpus": "stackexchange", "1_conv_id": "a78fca47acfe43b9a74361b3c222464e", "1_model_name": "GritLM/GritLM-7B", "1_prompt": "one flew over the cuckoo's nest why did chief pretend to be deaf?", "1_output": [["one flew over the cuckoo's nest why did chief pretend to be deaf?", "Scifi Stackexchange\n\nQ: Why did V need the police chief? In the film V for Vendetta, the following conversation takes place:\n\nFinch: Rookwood. Why didn't you come forward before? What were you waiting for?\nV: For you, Inspector. I needed you.\n\nI have tried to come up with a reason why V needed him, and have concluded that V didn't need him. Rather the author needed him (the police chief) to provide another perspective on the plot: a plot device.\nIs that reasonable?\n\nA: V talked of liberating people by giving them the truth. What better way or what more symbolic way to do that than to help the police inspector find the truth? And also, I think V hoped that the police inspector would be inspired to make the world a more just place if he knew the whole truth. V knew the world would go on once he died and someone needed to take his place in ensuring justice is served. That would be Evey with the help of the police inspector. \n"]], "1_corpus": "stackexchange"}
-{"tstamp": 1722445946.6832, "task_type": "retrieval", "type": "tievote", "models": ["nomic-ai/nomic-embed-text-v1.5", "GritLM/GritLM-7B"], "ip": "", "0_conv_id": "8d4a23f996e843a29567c43a3eec5b23", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": "who was the original host of what's my line", "0_output": [["who was the original host of what's my line", "What's My Line?\n\nStarting in July 1959 and continuing until July 1967, the show would occasionally record episodes onto quadruplex videotape for playback at a future date. In July 1959, this was state-of-the-art technology. At that time, the immediate concern of Mark Goodson and Bill Todman was that John Daly, anchor of the ABC network's nightly newscasts, would be allowed to visit Moscow to cover, in that capacity, a breaking news story. While Daly moderated the first live episode after his return from Moscow, he praised his employers' use of videotape. In such instances, cast and crew worked on two episodes consecutively during the same Sunday night: the \"taped\" one, followed immediately by the \"live\" one. The cast and crew began taking \"summer breaks\" from the show in July 1961, through July 1967. The closing credits of each prerecorded episode included an acknowledgment of the prerecorded status by the offscreen announcer.\nHosts and panelists\nThe host, then called the moderator, was veteran radio and television newsman John Charles Daly. Clifton Fadiman, Eamonn Andrews, and Random House co-founding publisher and panelist Bennett Cerf substituted on the four occasions when Daly was unavailable.\nThe show featured a panel of four celebrities who questioned the contestants. On the initial program of February 2, 1950, the panel comprised former New Jersey governor Harold Hoffman, columnist Dorothy Kilgallen, poet Louis Untermeyer, and psychiatrist Richard Hoffmann.  The panel varied somewhat in the following weeks, but after the first few broadcasts, during the show's earliest period the panel generally consisted of Kilgallen, actress Arlene Francis, Untermeyer and comedy writer Hal Block. Publisher Bennett Cerf replaced Untermeyer as a regular panelist in 1951, and comedian Steve Allen replaced Block in 1953.  Allen left in 1954 to launch The Tonight Show, and he was replaced by comedian Fred Allen (no relation), who remained on the panel until his death in 1956."]], "0_corpus": "wikipedia", "1_conv_id": "f3b21c3ce29d447f9701d499a4a05d8f", "1_model_name": "GritLM/GritLM-7B", "1_prompt": "who was the original host of what's my line", "1_output": [["who was the original host of what's my line", "What's My Line?\n\nMost notably, the mystery guests ran the gamut of show business' most legendary icons, including Jack Benny, John Wayne, Gary Cooper, Elizabeth Taylor, Ella Fitzgerald, Ava Gardner, Joan Crawford, James Cagney, Julie Andrews, Jack Lemmon, Jackie Gleason, Steve McQueen, Alfred Hitchcock, Doris Day, James Garner, Ronald Reagan, Jane Russell, Rosalind Russell, Fred Astaire, George Raft, Edward G. Robinson, Gene Autry, Lena Horne, Paul Newman and Joanne Woodward, Andy Griffith, Harry Belafonte, Ginger Rogers, Roy Rogers, Lucille Ball, Desi Arnaz, Bob Hope, Frank Sinatra, Dean Martin and Jerry Lewis, Sammy Davis Jr., Peter Lawford, Sean Connery, James Stewart, Henry Fonda, Salvador Dalí, Aretha Franklin, and many more.\nStyle\nWhat's My Line? is known for its attention to manners and class. In its early years, business suits and street dresses were worn by the host and panelists, but by 1953, the men normally wore black suits with bow tie (a few guests in fact wore black tie) while female panelists donned formal gown and often gloves. Exceptions to this dress code were on the broadcasts immediately following the deaths of Fred Allen and Dorothy Kilgallen, in which the male cast members wore straight neckties and the women wore simpler dresses.\nThe game followed a line of formality and adherence to rules. Although using first names at other points, Daly usually addressed using surnames when passing the questioning to a particular panelist. He would also amiably chide the panel if they began a conference without first asking him."]], "1_corpus": "wikipedia"}
-{"tstamp": 1722462631.9644, "task_type": "retrieval", "type": "leftvote", "models": ["GritLM/GritLM-7B", "BAAI/bge-large-en-v1.5"], "ip": "", "0_conv_id": "12e287a8ebd64b31a453aa9f5a03ac39", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "third president of the united states of america", "0_output": [["third president of the united states of america", "Thomas Jefferson\n\nThomas Jefferson (April 13, 1743 – July 4, 1826) was an American statesman, diplomat, lawyer, architect, philosopher, and Founding Father who served as the third president of the United States from 1801 to 1809. He was the primary author of the Declaration of Independence. Following the American Revolutionary War and prior to becoming president in 1801, Jefferson was the nation's first U.S. secretary of state under George Washington and then the nation's second vice president under John Adams. Jefferson was a leading proponent of democracy, republicanism, and individual rights, and produced formative documents and decisions at the state, national, and international levels. His writings and advocacy for human rights, including freedom of thought, speech, and religion, served as substantial inspirations to the American Revolution and subsequent Revolutionary War in which the Thirteen Colonies succeeded in breaking from British America and establishing the United States as a sovereign nation.\nDuring the American Revolution, Jefferson represented Virginia at the Second Continental Congress and served as the second governor of Virginia from 1779 to 1781. In 1785, Congress appointed Jefferson U.S. minister to France, where he served from 1785 to 1789. President Washington then appointed Jefferson the nation's first secretary of state, where he served from 1790 to 1793. During this time, in the early 1790s, Jefferson and James Madison organized the Democratic-Republican Party to oppose the Federalist Party during the formation of the nation's First Party System. Jefferson and Federalist John Adams became both friends and political rivals. In the 1796 U.S. presidential election between the two, Jefferson came in second, which made him Adams' vice president under the electoral laws of the time. Four years later, in the 1800 presidential election, Jefferson again challenged Adams, and won the presidency. In 1804, Jefferson was reelected overwhelmingly to a second term."]], "0_corpus": "wikipedia", "1_conv_id": "60a20f07742c4a3f8aa9365dd29b939f", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "third president of the united states of america", "1_output": [["third president of the united states of america", "President of the United States\n\nPresidents have traditionally placed one hand upon a Bible while taking the oath, and have added \"So help me God\" to the end of the oath. Although the oath may be administered by any person authorized by law to administer oaths, presidents are traditionally sworn in by the chief justice of the United States.\nIncumbency\nTerm limit\nWhen the first president, George Washington, announced in his Farewell Address that he was not running for a third term, he established a \"two terms then out\" precedent. Precedent became tradition after Thomas Jefferson publicly embraced the principle a decade later during his second term, as did his two immediate successors, James Madison and James Monroe. In spite of the strong two-term tradition, Ulysses S. Grant sought nomination at the 1880 Republican National Convention for a non-consecutive third term, but was unsuccessful.\nIn 1940, after leading the nation through the Great Depression and focused on supporting U.S. allied nations at war with the Axis powers, Franklin Roosevelt was elected to a third term, breaking the long-standing precedent. Four years later, with the U.S. engaged in World War II, he was re-elected again despite his declining physical health; he died 82 days into his fourth term on April 12, 1945."]], "1_corpus": "wikipedia"}
+{"tstamp": 1722445946.6832, "task_type": "retrieval", "type": "tievote", "models": ["nomic-ai/nomic-embed-text-v1.5", "GritLM/GritLM-7B"], "ip": "", "0_conv_id": "8d4a23f996e843a29567c43a3eec5b23", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": "who was the original host of what's my line", "0_output": [["who was the original host of what's my line", "What's My Line?\n\nStarting in July 1959 and continuing until July 1967, the show would occasionally record episodes onto quadruplex videotape for playback at a future date. In July 1959, this was state-of-the-art technology. At that time, the immediate concern of Mark Goodson and Bill Todman was that John Daly, anchor of the ABC network's nightly newscasts, would be allowed to visit Moscow to cover, in that capacity, a breaking news story. While Daly moderated the first live episode after his return from Moscow, he praised his employers' use of videotape. In such instances, cast and crew worked on two episodes consecutively during the same Sunday night: the \"taped\" one, followed immediately by the \"live\" one. The cast and crew began taking \"summer breaks\" from the show in July 1961, through July 1967. The closing credits of each prerecorded episode included an acknowledgment of the prerecorded status by the offscreen announcer.\nHosts and panelists\nThe host, then called the moderator, was veteran radio and television newsman John Charles Daly. Clifton Fadiman, Eamonn Andrews, and Random House co-founding publisher and panelist Bennett Cerf substituted on the four occasions when Daly was unavailable.\nThe show featured a panel of four celebrities who questioned the contestants. On the initial program of February 2, 1950, the panel comprised former New Jersey governor Harold Hoffman, columnist Dorothy Kilgallen, poet Louis Untermeyer, and psychiatrist Richard Hoffmann.  The panel varied somewhat in the following weeks, but after the first few broadcasts, during the show's earliest period the panel generally consisted of Kilgallen, actress Arlene Francis, Untermeyer and comedy writer Hal Block. Publisher Bennett Cerf replaced Untermeyer as a regular panelist in 1951, and comedian Steve Allen replaced Block in 1953.  Allen left in 1954 to launch The Tonight Show, and he was replaced by comedian Fred Allen (no relation), who remained on the panel until his death in 1956."]], "0_corpus": "wikipedia", "1_conv_id": "f3b21c3ce29d447f9701d499a4a05d8f", "1_model_name": "GritLM/GritLM-7B", "1_prompt": "who was the original host of what's my line", "1_output": [["who was the original host of what's my line", "What's My Line?\n\nMost notably, the mystery guests ran the gamut of show business' most legendary icons, including Jack Benny, John Wayne, Gary Cooper, Elizabeth Taylor, Ella Fitzgerald, Ava Gardner, Joan Crawford, James Cagney, Julie Andrews, Jack Lemmon, Jackie Gleason, Steve McQueen, Alfred Hitchcock, Doris Day, James Garner, Ronald Reagan, Jane Russell, Rosalind Russell, Fred Astaire, George Raft, Edward G. Robinson, Gene Autry, Lena Horne, Paul Newman and Joanne Woodward, Andy Griffith, Harry Belafonte, Ginger Rogers, Roy Rogers, Lucille Ball, Desi Arnaz, Bob Hope, Frank Sinatra, Dean Martin and Jerry Lewis, Sammy Davis Jr., Peter Lawford, Sean Connery, James Stewart, Henry Fonda, Salvador Dal\u00ed, Aretha Franklin, and many more.\nStyle\nWhat's My Line? is known for its attention to manners and class. In its early years, business suits and street dresses were worn by the host and panelists, but by 1953, the men normally wore black suits with bow tie (a few guests in fact wore black tie) while female panelists donned formal gown and often gloves. Exceptions to this dress code were on the broadcasts immediately following the deaths of Fred Allen and Dorothy Kilgallen, in which the male cast members wore straight neckties and the women wore simpler dresses.\nThe game followed a line of formality and adherence to rules. Although using first names at other points, Daly usually addressed using surnames when passing the questioning to a particular panelist. He would also amiably chide the panel if they began a conference without first asking him."]], "1_corpus": "wikipedia"}
+{"tstamp": 1722462631.9644, "task_type": "retrieval", "type": "leftvote", "models": ["GritLM/GritLM-7B", "BAAI/bge-large-en-v1.5"], "ip": "", "0_conv_id": "12e287a8ebd64b31a453aa9f5a03ac39", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "third president of the united states of america", "0_output": [["third president of the united states of america", "Thomas Jefferson\n\nThomas Jefferson (April 13, 1743 \u2013 July 4, 1826) was an American statesman, diplomat, lawyer, architect, philosopher, and Founding Father who served as the third president of the United States from 1801 to 1809. He was the primary author of the Declaration of Independence. Following the American Revolutionary War and prior to becoming president in 1801, Jefferson was the nation's first U.S. secretary of state under George Washington and then the nation's second vice president under John Adams. Jefferson was a leading proponent of democracy, republicanism, and individual rights, and produced formative documents and decisions at the state, national, and international levels. His writings and advocacy for human rights, including freedom of thought, speech, and religion, served as substantial inspirations to the American Revolution and subsequent Revolutionary War in which the Thirteen Colonies succeeded in breaking from British America and establishing the United States as a sovereign nation.\nDuring the American Revolution, Jefferson represented Virginia at the Second Continental Congress and served as the second governor of Virginia from 1779 to 1781. In 1785, Congress appointed Jefferson U.S. minister to France, where he served from 1785 to 1789. President Washington then appointed Jefferson the nation's first secretary of state, where he served from 1790 to 1793. During this time, in the early 1790s, Jefferson and James Madison organized the Democratic-Republican Party to oppose the Federalist Party during the formation of the nation's First Party System. Jefferson and Federalist John Adams became both friends and political rivals. In the 1796 U.S. presidential election between the two, Jefferson came in second, which made him Adams' vice president under the electoral laws of the time. Four years later, in the 1800 presidential election, Jefferson again challenged Adams, and won the presidency. In 1804, Jefferson was reelected overwhelmingly to a second term."]], "0_corpus": "wikipedia", "1_conv_id": "60a20f07742c4a3f8aa9365dd29b939f", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "third president of the united states of america", "1_output": [["third president of the united states of america", "President of the United States\n\nPresidents have traditionally placed one hand upon a Bible while taking the oath, and have added \"So help me God\" to the end of the oath. Although the oath may be administered by any person authorized by law to administer oaths, presidents are traditionally sworn in by the chief justice of the United States.\nIncumbency\nTerm limit\nWhen the first president, George Washington, announced in his Farewell Address that he was not running for a third term, he established a \"two terms then out\" precedent. Precedent became tradition after Thomas Jefferson publicly embraced the principle a decade later during his second term, as did his two immediate successors, James Madison and James Monroe. In spite of the strong two-term tradition, Ulysses S. Grant sought nomination at the 1880 Republican National Convention for a non-consecutive third term, but was unsuccessful.\nIn 1940, after leading the nation through the Great Depression and focused on supporting U.S. allied nations at war with the Axis powers, Franklin Roosevelt was elected to a third term, breaking the long-standing precedent. Four years later, with the U.S. engaged in World War II, he was re-elected again despite his declining physical health; he died 82 days into his fourth term on April 12, 1945."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722466236.4161, "task_type": "retrieval", "type": "share", "models": ["### Model A: sentence-transformers/all-MiniLM-L6-v2", "### Model B: BM25"], "ip": "", "0_conv_id": "22b61881dfef4ad7b85c03791a1d7af1", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "A method enabling domain adaptation and zero-shot translation in NMT via parameter generation.", "0_output": [["A method enabling domain adaptation and zero-shot translation in NMT via parameter generation.", "Title: The Information Loss Problem of Black Hole and the First Order Phase Transition in String Theory\n\nAbstract: In recent years, Susskind, Thorlacius and Uglum have proposed a model for strings near a black hole horizon in order to represent the quantum mechanical entropy of the black hole and to resolve the information loss problem. However, this model is insufficient because they did not consider the metric modification due to massive strings and did not explain how to carry information from inside of the horizon to the outside world. In this paper, we present a possible, intuitive model for the time development of a black hole in order to solve the information loss problem. In this model, we assume that a first order phase transition occurs near the Hagedorn temperature and the string gas changes to hypothetical matter with vanishing entropy and energy which we call `the Planck solid'. We also study the background geometry of black holes in this picture and find out that there is no singularity within the model."]], "0_corpus": "arxiv", "1_conv_id": "c95791b1917c4f9297bc8e7a7badf23a", "1_model_name": "BM25", "1_prompt": "A method enabling domain adaptation and zero-shot translation in NMT via parameter generation.", "1_output": [["A method enabling domain adaptation and zero-shot translation in NMT via parameter generation.", "Title: Generalised Unsupervised Domain Adaptation of Neural Machine Translation with Cross-Lingual Data Selection\n\nAbstract: This paper considers the unsupervised domain adaptation problem for neural machine translation (NMT), where we assume the access to only monolingual text in either the source or target language in the new domain. We propose a cross-lingual data selection method to extract in-domain sentences in the missing language side from a large generic monolingual corpus. Our proposed method trains an adaptive layer on top of multilingual BERT by contrastive learning to align the representation between the source and target language. This then enables the transferability of the domain classifier between the languages in a zero-shot manner. Once the in-domain data is detected by the classifier, the NMT model is then adapted to the new domain by jointly learning translation and domain discrimination tasks. We evaluate our cross-lingual data selection method on NMT across five diverse domains in three language pairs, as well as a real-world scenario of translation for COVID-19. The results show that our proposed method outperforms other selection baselines up to +1.5 BLEU score."]], "1_corpus": "arxiv"}
-{"tstamp": 1722466337.7615, "task_type": "retrieval", "type": "rightvote", "models": ["GritLM/GritLM-7B", "mixedbread-ai/mxbai-embed-large-v1"], "ip": "", "0_conv_id": "dfba96c8f4894e7ebe55944dc5dcf204", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "what is the difference between c major and d major?", "0_output": [["what is the difference between c major and d major?", "Major second\n\nIn Western music theory, a major second (sometimes also called whole tone or a whole step)  is a second spanning two semitones (). A second is a musical interval encompassing two adjacent staff positions (see Interval number for more details). For example, the interval from C to D is a major second, as the note D lies two semitones above C, and the two notes are notated on adjacent staff positions. Diminished, minor and augmented seconds are notated on adjacent staff positions as well, but consist of a different number of semitones (zero, one, and three).\nThe major second is the interval that occurs between the first and second degrees of a major scale, the tonic and the supertonic. On a musical keyboard, a major second is the interval between two keys separated by one key, counting white and black keys alike. On a guitar string, it is the interval separated by two frets. In moveable-do solfège, it is the interval between do and re. It is considered a melodic step, as opposed to larger intervals called skips.\nIntervals composed of two semitones, such as the major second and the diminished third, are also called tones, whole tones, or whole steps.\nIn just intonation, major seconds can occur in at least two different frequency ratios:"]], "0_corpus": "wikipedia", "1_conv_id": "352adfff03c14ef883e0adb669b9b5ca", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "what is the difference between c major and d major?", "1_output": [["what is the difference between c major and d major?", "C major\n\nC major (or the key of C) is a major scale based on C, consisting of the pitches C, D, E, F, G, A, and B. C major is one of the most common keys used in music. Its key signature has  no flats or sharps. Its relative minor is A minor and its parallel minor is C minor.\nThe C major scale is:\nOn the piano, the C major scale can be played by playing only the white keys starting on C.\nScale degree chords\nThe scale-degree chords of C major are:\nTonic – C major\nSupertonic – D minor\nMediant – E minor\nSubdominant – F major\nDominant – G major\nSubmediant – A minor\nLeading-tone – B diminished"]], "1_corpus": "wikipedia"}
-{"tstamp": 1722477634.9431, "task_type": "retrieval", "type": "bothbadvote", "models": ["Alibaba-NLP/gte-Qwen2-7B-instruct", "BM25"], "ip": "", "0_conv_id": "230ef6844ca145b3aeb2836e1cbc7c0e", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": "average weight of a bee", "0_output": [["average weight of a bee", "Lincoln Park Zoo\n\nWaterfowl Lagoon A lagoon for waterfowl features the zoo's familiar flock of almost fifty Chilean flamingos, who use the exhibit's mudflaps to build nests and use the indoor Flamingo Habitat, sometimes referred to as the Flamingo Dome, during the winter to hide from the cold. It is also home to swan geese.\nChilean flamingo\nSwan goose\nHope B. McCormick Swan Pond\nLincoln Park Zoo began with the gift of a pair of swans, and to commemorate its founding and that special moment in history, a pair of trumpeter swans have continued to make their home at the zoo, now in the Hope B. McCormick Swan Pond, as a reminder of the zoo's long history. Several diverse species of waterfowl call this pond home.\nBarrow's goldeneye\nHooded merganser\nNorthern pintail\nRuddy duck\nTrumpeter swan\nFarm-in-the-Zoo\nThe Farm-in-the-Zoo Presented by John Deere is across the South Pond from the rest of the zoo, and is designed to \"give Chicago Kids a chance to experience a bit of the country in the city.\" Opened in 1964, it exhibits pigs, cows, horses and other domestic animals. Visitors can pet and feed the animals and roam vegetable gardens. Each day, the cows are milked in public and staff are on hand to explain other elements of farm life. Interactive exhibits allow guests to \"hatch\" from an egg, learn about farm weather, and about gardening.\nChicken\nCattle\nDomestic rabbit\nGoat\nPig\nPony\nWestern honey bee"]], "0_corpus": "wikipedia", "1_conv_id": "acfea7cbcc524fdeb3b29df0e9dd7321", "1_model_name": "BM25", "1_prompt": "average weight of a bee", "1_output": [["average weight of a bee", "Bee hummingbird\n\nThe bee hummingbird, zunzuncito or Helena hummingbird (Mellisuga helenae) is a species of hummingbird, native to the island of Cuba in the Caribbean. It is the smallest known bird. The bee hummingbird feeds on nectar of flowers and bugs found in Cuba.\nDescription\nThe bee hummingbird is the smallest living bird. Females weigh  and are  long, and are slightly larger than males, which have an average weight of  and length of . Like all hummingbirds, it is a swift, strong flier."]], "1_corpus": "wikipedia"}
-{"tstamp": 1722480982.9783, "task_type": "retrieval", "type": "rightvote", "models": ["nomic-ai/nomic-embed-text-v1.5", "voyage-multilingual-2"], "ip": "", "0_conv_id": "e55e68602ed84754bfcad23227f5aba5", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": "what nfl coach has the most wins ever", "0_output": [["what nfl coach has the most wins ever", "List of current NFL head coaches\n\nThe table shows the current coaches and their records for each National Football League (NFL) team. The longest tenured head coach on his current team is Mike Tomlin who has been with the Pittsburgh Steelers since the 2007 NFL season. Andy Reid is the only active coach with multiple Super Bowl wins with three. Other active head coaches to have won a Super Bowl are Tomlin, John Harbaugh, Mike McCarthy, Sean Payton, Doug Pederson, and Sean McVay. Reid, Tomlin, Harbaugh, and McVay have won Super Bowls with their current teams, while McCarthy, Payton, and Pederson have won with their former teams."]], "0_corpus": "wikipedia", "1_conv_id": "885750a842f640f4940411cc73abf61d", "1_model_name": "voyage-multilingual-2", "1_prompt": "what nfl coach has the most wins ever", "1_output": [["what nfl coach has the most wins ever", "List of NFL head coach wins leaders\n\nThe following is a list of the top National Football League (NFL) head coaches in wins.\nDon Shula holds the current records for regular season wins at 328. Shula’s tenure included many 12 game seasons, thus his win total took longer to amass than that of currently active coaches. Bill Belichick holds the record for postseason wins at 31.\nAmong active head coaches, Andy Reid is the leader in regular season and postseason wins, with 258 and 26, respectively.\nRegular season wins list\nThis sortable table shows the top 100 NFL head coaches in order of total regular season wins. The table also shows every team for which he was a head coach and his record with each team.\nWhen sorting by team, coaches are listed in order of wins for that team.\nTeams Grouped Together:\nThe NFL did not officially count ties in the standings until .  Therefore, ties occurring prior to 1972 do not count toward a coach's win percentage, while ties occurring 1972 or later count as half-win, half-loss.\nDoes not include Paul Brown's four-year AAFC record of 47–4–3.\nIn 2012, Bruce Arians served as acting head coach of the Indianapolis Colts for 12 games as head coach Chuck Pagano was on medical leave. The Colts' 9–3 record in these games is credited to Pagano and is reflected in the totals on this chart.\nDoes not include Ray Flaherty's four-year AAFC record of 26–16–2."]], "1_corpus": "wikipedia"}
-{"tstamp": 1722481054.2018, "task_type": "retrieval", "type": "leftvote", "models": ["nomic-ai/nomic-embed-text-v1.5", "voyage-multilingual-2"], "ip": "", "0_conv_id": "ea6cf2ca88504e8c958323c6a22653e6", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": "is cpu and core same?", "0_output": [["is cpu and core same?", "Superuser Stackexchange\n\nQ: What is the difference between CPU and Microprocessor? What is the difference between CPU and Microprocessor?\n\nA: In everyday use: nothing, just the name.\nIf you want to be correct: a CPU is the \"main\" microprocessor, if you have several combined - the one that controls the others. Hence, Central Processing Unit.\n"]], "0_corpus": "stackexchange", "1_conv_id": "9e7db436dcc347bea7ce3768e360e77c", "1_model_name": "voyage-multilingual-2", "1_prompt": "is cpu and core same?", "1_output": [["is cpu and core same?", "Serverfault Stackexchange\n\nQ: Difference between Processors and CPU core can any one please clarify the difference between actual no of processors and CPU cores.\nin my case i am having Intel Xeon® Processor E5620 as per intels product page: its having\n# of Cores 4 \n\n# of Threads 8\n\nand                  cat /proc/cpuinfo |grep processor|wc -l is showing 16 \nso what is this 16 showing here?.\nI am planning to virtualize this machine with xen to 4 vms,each with 4 cpu.So it will be better if i can understand about these values and assigning vcpus to guest vm.\n\nA: An E5620 CPU has 4 cores on the die. With Hyper-Threading turned on, that gives you 8 threads.\nPerhaps your machine has 2 physical E5620 CPUs? If it's not powered on, try opening the case and counting them.\nIf it's powered up and you can log in, try this: cat /proc/cpuinfo | grep “physical id” | sort | uniq | wc -l\n2 physical CPUs x 4 cores/CPU x 2 hyper-threads/core = 16 threads\n"]], "1_corpus": "stackexchange"}
+{"tstamp": 1722466337.7615, "task_type": "retrieval", "type": "rightvote", "models": ["GritLM/GritLM-7B", "mixedbread-ai/mxbai-embed-large-v1"], "ip": "", "0_conv_id": "dfba96c8f4894e7ebe55944dc5dcf204", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "what is the difference between c major and d major?", "0_output": [["what is the difference between c major and d major?", "Major second\n\nIn Western music theory, a major second (sometimes also called whole tone or a whole step)  is a second spanning two semitones (). A second is a musical interval encompassing two adjacent staff positions (see Interval number for more details). For example, the interval from C to D is a major second, as the note D lies two semitones above C, and the two notes are notated on adjacent staff positions. Diminished, minor and augmented seconds are notated on adjacent staff positions as well, but consist of a different number of semitones (zero, one, and three).\nThe major second is the interval that occurs between the first and second degrees of a major scale, the tonic and the supertonic. On a musical keyboard, a major second is the interval between two keys separated by one key, counting white and black keys alike. On a guitar string, it is the interval separated by two frets. In moveable-do solf\u00e8ge, it is the interval between do and re. It is considered a melodic step, as opposed to larger intervals called skips.\nIntervals composed of two semitones, such as the major second and the diminished third, are also called tones, whole tones, or whole steps.\nIn just intonation, major seconds can occur in at least two different frequency ratios:"]], "0_corpus": "wikipedia", "1_conv_id": "352adfff03c14ef883e0adb669b9b5ca", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "what is the difference between c major and d major?", "1_output": [["what is the difference between c major and d major?", "C major\n\nC major (or the key of C) is a major scale based on C, consisting of the pitches C, D, E, F, G, A, and B. C major is one of the most common keys used in music. Its key signature has  no flats or sharps. Its relative minor is A minor and its parallel minor is C minor.\nThe C major scale is:\nOn the piano, the C major scale can be played by playing only the white keys starting on C.\nScale degree chords\nThe scale-degree chords of C major are:\nTonic \u2013 C major\nSupertonic \u2013 D minor\nMediant \u2013 E minor\nSubdominant \u2013 F major\nDominant \u2013 G major\nSubmediant \u2013 A minor\nLeading-tone \u2013 B diminished"]], "1_corpus": "wikipedia"}
+{"tstamp": 1722480982.9783, "task_type": "retrieval", "type": "rightvote", "models": ["nomic-ai/nomic-embed-text-v1.5", "voyage-multilingual-2"], "ip": "", "0_conv_id": "e55e68602ed84754bfcad23227f5aba5", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": "what nfl coach has the most wins ever", "0_output": [["what nfl coach has the most wins ever", "List of current NFL head coaches\n\nThe table shows the current coaches and their records for each National Football League (NFL) team. The longest tenured head coach on his current team is Mike Tomlin who has been with the Pittsburgh Steelers since the 2007 NFL season. Andy Reid is the only active coach with multiple Super Bowl wins with three. Other active head coaches to have won a Super Bowl are Tomlin, John Harbaugh, Mike McCarthy, Sean Payton, Doug Pederson, and Sean McVay. Reid, Tomlin, Harbaugh, and McVay have won Super Bowls with their current teams, while McCarthy, Payton, and Pederson have won with their former teams."]], "0_corpus": "wikipedia", "1_conv_id": "885750a842f640f4940411cc73abf61d", "1_model_name": "voyage-multilingual-2", "1_prompt": "what nfl coach has the most wins ever", "1_output": [["what nfl coach has the most wins ever", "List of NFL head coach wins leaders\n\nThe following is a list of the top National Football League (NFL) head coaches in wins.\nDon Shula holds the current records for regular season wins at 328. Shula\u2019s tenure included many 12 game seasons, thus his win total took longer to amass than that of currently active coaches. Bill Belichick holds the record for postseason wins at 31.\nAmong active head coaches, Andy Reid is the leader in regular season and postseason wins, with 258 and 26, respectively.\nRegular season wins list\nThis sortable table shows the top 100 NFL head coaches in order of total regular season wins. The table also shows every team for which he was a head coach and his record with each team.\nWhen sorting by team, coaches are listed in order of wins for that team.\nTeams Grouped Together:\nThe NFL did not officially count ties in the standings until .  Therefore, ties occurring prior to 1972 do not count toward a coach's win percentage, while ties occurring 1972 or later count as half-win, half-loss.\nDoes not include Paul Brown's four-year AAFC record of 47\u20134\u20133.\nIn 2012, Bruce Arians served as acting head coach of the Indianapolis Colts for 12 games as head coach Chuck Pagano was on medical leave. The Colts' 9\u20133 record in these games is credited to Pagano and is reflected in the totals on this chart.\nDoes not include Ray Flaherty's four-year AAFC record of 26\u201316\u20132."]], "1_corpus": "wikipedia"}
+{"tstamp": 1722481054.2018, "task_type": "retrieval", "type": "leftvote", "models": ["nomic-ai/nomic-embed-text-v1.5", "voyage-multilingual-2"], "ip": "", "0_conv_id": "ea6cf2ca88504e8c958323c6a22653e6", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": "is cpu and core same?", "0_output": [["is cpu and core same?", "Superuser Stackexchange\n\nQ: What is the difference between CPU and Microprocessor? What is the difference between CPU and Microprocessor?\n\nA: In everyday use: nothing, just the name.\nIf you want to be correct: a CPU is the \"main\" microprocessor, if you have several combined - the one that controls the others. Hence, Central Processing Unit.\n"]], "0_corpus": "stackexchange", "1_conv_id": "9e7db436dcc347bea7ce3768e360e77c", "1_model_name": "voyage-multilingual-2", "1_prompt": "is cpu and core same?", "1_output": [["is cpu and core same?", "Serverfault Stackexchange\n\nQ: Difference between Processors and CPU core can any one please clarify the difference between actual no of processors and CPU cores.\nin my case i am having Intel Xeon\u00ae Processor E5620 as per intels product page: its having\n# of Cores 4 \n\n# of Threads 8\n\nand                  cat /proc/cpuinfo |grep processor|wc -l is showing 16 \nso what is this 16 showing here?.\nI am planning to virtualize this machine with xen to 4 vms,each with 4 cpu.So it will be better if i can understand about these values and assigning vcpus to guest vm.\n\nA: An E5620 CPU has 4 cores on the die. With Hyper-Threading turned on, that gives you 8 threads.\nPerhaps your machine has 2 physical E5620 CPUs? If it's not powered on, try opening the case and counting them.\nIf it's powered up and you can log in, try this: cat /proc/cpuinfo | grep \u201cphysical id\u201d | sort | uniq | wc -l\n2 physical CPUs x 4 cores/CPU x 2 hyper-threads/core = 16 threads\n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1722481084.1046, "task_type": "retrieval", "type": "leftvote", "models": ["nomic-ai/nomic-embed-text-v1.5", "voyage-multilingual-2"], "ip": "", "0_conv_id": "82743db7049a4ca6bb8cf42a5030e4de", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": "A technique to enable efficient federated learning on mobile devices by compressing gradients up to 600x without accuracy loss.", "0_output": [["A technique to enable efficient federated learning on mobile devices by compressing gradients up to 600x without accuracy loss.", "Title: FedGreen: Federated Learning with Fine-Grained Gradient Compression for Green Mobile Edge Computing\n\nAbstract: Federated learning (FL) enables devices in mobile edge computing (MEC) to collaboratively train a shared model without uploading the local data. Gradient compression may be applied to FL to alleviate the communication overheads but current FL with gradient compression still faces great challenges. To deploy green MEC, we propose FedGreen, which enhances the original FL with fine-grained gradient compression to efficiently control the total energy consumption of the devices. Specifically, we introduce the relevant operations including device-side gradient reduction and server-side element-wise aggregation to facilitate the gradient compression in FL. According to a public dataset, we investigate the contributions of the compressed local gradients with respect to different compression ratios. After that, we formulate and tackle a learning accuracy-energy efficiency tradeoff problem where the optimal compression ratio and computing frequency are derived for each device. Experiments results demonstrate that given the 80% test accuracy requirement, compared with the baseline schemes, FedGreen reduces at least 32% of the total energy consumption of the devices."]], "0_corpus": "arxiv", "1_conv_id": "e809a23f41f645d3abb5af947849accf", "1_model_name": "voyage-multilingual-2", "1_prompt": "A technique to enable efficient federated learning on mobile devices by compressing gradients up to 600x without accuracy loss.", "1_output": [["A technique to enable efficient federated learning on mobile devices by compressing gradients up to 600x without accuracy loss.", "Title: Wyner-Ziv Gradient Compression for Federated Learning\n\nAbstract: Due to limited communication resources at the client and a massive number of model parameters, large-scale distributed learning tasks suffer from communication bottleneck. Gradient compression is an effective method to reduce communication load by transmitting compressed gradients. Motivated by the fact that in the scenario of stochastic gradients descent, gradients between adjacent rounds may have a high correlation since they wish to learn the same model, this paper proposes a practical gradient compression scheme for federated learning, which uses historical gradients to compress gradients and is based on Wyner-Ziv coding but without any probabilistic assumption. We also implement our gradient quantization method on the real dataset, and the performance of our method is better than the previous schemes."]], "1_corpus": "arxiv"}
 {"tstamp": 1722509487.58, "task_type": "retrieval", "type": "share", "models": ["jinaai/jina-embeddings-v2-base-en", "BM25"], "ip": "", "0_conv_id": "d3319985335f4cd1a28138e55cad9c02", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "what is difference between quick oats and old fashioned?", "0_output": [["what is difference between quick oats and old fashioned?", "Stackoverflow Stackexchange\n\nQ: Using msys shell in Visual Studio Code I'm getting into Visual Studio Code as I like its lightweight design and flexibility. It has an integrated terminal that can run, I believe, any shell [see here]. I'd like to use it run the msys shell that I've been using lately. Setting the terminal to run another shell other than the default powershell.exe should be as simple as changing the settings as described [here]. However, I use the msys shell by running the msys.bat file, as recommended on mingw.org.\nSo my question is, where is the .exe file I need to set the terminal to the msys shell? Or is running this shell without the .bat in this fashion impossible? Thanks!\n\nA: according to MSYS terminals, we can use this:\n    \"terminal.integrated.profiles.windows\": {\n        \"msys mingw64\": {\n            \"path\": \"C:/msys64/msys2_shell.cmd\",\n            \"args\": [\n                \"-defterm\",\n                \"-here\",\n                \"-no-start\",\n                \"-mingw64\"\n            ]\n        }\n    },\n\nthis starts msys mingw64. to start other shell, just change -mingw64 to -mingw32 or -msys.\n"]], "0_corpus": "stackexchange", "1_conv_id": "a2d2abe768b045158e1e1b427280021f", "1_model_name": "BM25", "1_prompt": "what is difference between quick oats and old fashioned?", "1_output": [["what is difference between quick oats and old fashioned?", "Stackoverflow Stackexchange\n\nQ: Any one successfully compiled OAT using Android 4.4 ART with LLVM backend? Short story: success with default backend, no luck with LLVM backend. Did anyone successfully compiled OAT using LLVM backend (--compiler-backend=Portable)?\nDetails:\nOn Android 4.4, I successfully compiled a DEX file to OAT file using the default \"Quick\" backend, which is the MIR based. When I tried to use LLVM backend \"Portable\" to do the same thing, there are two problems:\n\n\n*\n\n*When used in the same way as the Quick backend compiled OAT file, the LLVM backend compiled OAT file make the app segfault.\n\n*LLVM compiled really slow, comparing with default Quick option. Methods took over 100ms to compile in LLVM, and a small DEX takes 1 min to finish. Default Quick backend only uses 2-3 seconds to finish the whole compiling.\nAnybody went through this before?\n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1722521647.0611, "task_type": "retrieval", "type": "rightvote", "models": ["GritLM/GritLM-7B", "Salesforce/SFR-Embedding-2_R"], "ip": "", "0_conv_id": "1c112c5e728246758a521e4f539df79c", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "How to compute a convex hull of a start set?", "0_output": [["How to compute a convex hull of a start set?", "Convex hull algorithms\n\nAlgorithms that construct convex hulls of various objects have a broad range of applications in mathematics and computer science.\nIn computational geometry, numerous algorithms are proposed for computing the convex hull of a finite set of points, with various computational complexities.\nComputing the convex hull means that a non-ambiguous and efficient representation of the required convex shape is constructed. The complexity of the corresponding algorithms is usually estimated in terms of n, the number of input points, and sometimes also in terms of h, the number of points on the convex hull."]], "0_corpus": "wikipedia", "1_conv_id": "ef9ca1f164bf4bf8a845800f6aba276d", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "How to compute a convex hull of a start set?", "1_output": [["How to compute a convex hull of a start set?", "Convex hull algorithms\n\nA later simplification by  and  uses only a single stack data structure. Their algorithm traverses the polygon clockwise, starting from its leftmost vertex. As it does, it stores a convex sequence of vertices on the stack, the ones that have not yet been identified as being within pockets. At each step, the algorithm follows a path along the polygon from the stack top to the next vertex that is not in one of the two pockets adjacent to the stack top. Then, while the top two vertices on the stack together with this new vertex are not in convex position, it pops the stack, before finally pushing the new vertex onto the stack. When the clockwise traversal reaches the starting point, the algorithm returns the sequence of stack vertices as the hull.\nHigher dimensions\nA number of algorithms are known for the three-dimensional case, as well as for arbitrary dimensions. Chan's algorithm is used for dimensions 2 and 3, and Quickhull is used for computation of the convex hull in higher dimensions.\nFor a finite set of points, the convex hull is a convex polyhedron in three dimensions, or in general a convex polytope for any number of dimensions, whose vertices are some of the points in the input set.  Its representation is not so simple as in the planar case, however. In higher dimensions, even if the vertices of a convex polytope are known, construction of its faces is a non-trivial task, as is the dual problem of constructing the vertices given the faces. The size of the output face information may be exponentially larger than the size of the input vertices, and even in cases where the input and output are both of comparable size the known algorithms for high-dimensional convex hulls are not output-sensitive due both to issues with degenerate inputs and with intermediate results of high complexity."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722521728.36, "task_type": "retrieval", "type": "tievote", "models": ["nomic-ai/nomic-embed-text-v1.5", "Salesforce/SFR-Embedding-2_R"], "ip": "", "0_conv_id": "088ed5dfe7194d848cd4e4ee79410a81", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": "What are some standard metrics used for new drug approval?", "0_output": [["What are some standard metrics used for new drug approval?", "Pharmaceutical industry\n\nProduct approval In the United States, new pharmaceutical products must be approved by the Food and Drug Administration (FDA) as being both safe and effective. This process generally involves submission of an Investigational New Drug filing with sufficient pre-clinical data to support proceeding with human trials. Following IND approval, three phases of progressively larger human clinical trials may be conducted. Phase I generally studies toxicity using healthy volunteers. Phase II can include pharmacokinetics and dosing in patients, and Phase III is a very large study of efficacy in the intended patient population. Following the successful completion of phase III testing, a New Drug Application is submitted to the FDA. The FDA reviews the data and if the product is seen as having a positive benefit-risk assessment, approval to market the product in the US is granted.\nA fourth phase of post-approval surveillance is also often required due to the fact that even the largest clinical trials cannot effectively predict the prevalence of rare side-effects. Postmarketing surveillance ensures that after marketing the safety of a drug is monitored closely. In certain instances, its indication may need to be limited to particular patient groups, and in others the substance is withdrawn from the market completely."]], "0_corpus": "wikipedia", "1_conv_id": "23ec2f7fd87e4121a06ee5fd7ba771f5", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "What are some standard metrics used for new drug approval?", "1_output": [["What are some standard metrics used for new drug approval?", "Pharmaceutical industry\n\nProduct approval In the United States, new pharmaceutical products must be approved by the Food and Drug Administration (FDA) as being both safe and effective. This process generally involves submission of an Investigational New Drug filing with sufficient pre-clinical data to support proceeding with human trials. Following IND approval, three phases of progressively larger human clinical trials may be conducted. Phase I generally studies toxicity using healthy volunteers. Phase II can include pharmacokinetics and dosing in patients, and Phase III is a very large study of efficacy in the intended patient population. Following the successful completion of phase III testing, a New Drug Application is submitted to the FDA. The FDA reviews the data and if the product is seen as having a positive benefit-risk assessment, approval to market the product in the US is granted.\nA fourth phase of post-approval surveillance is also often required due to the fact that even the largest clinical trials cannot effectively predict the prevalence of rare side-effects. Postmarketing surveillance ensures that after marketing the safety of a drug is monitored closely. In certain instances, its indication may need to be limited to particular patient groups, and in others the substance is withdrawn from the market completely."]], "1_corpus": "wikipedia"}
-{"tstamp": 1722521891.8626, "task_type": "retrieval", "type": "leftvote", "models": ["nomic-ai/nomic-embed-text-v1.5", "Salesforce/SFR-Embedding-2_R"], "ip": "", "0_conv_id": "6b01d2cbf4774ea2a4396a4d4a070129", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": "What animals are legally approved to be used for testing drugs?", "0_output": [["What animals are legally approved to be used for testing drugs?", "Animal testing\n\nDogs Dogs are widely used in biomedical research, testing, and education—particularly beagles, because they are gentle and easy to handle, and to allow for comparisons with historical data from beagles (a Reduction technique). They are used as models for human and veterinary diseases in cardiology, endocrinology, and bone and joint studies, research that tends to be highly invasive, according to the Humane Society of the United States. The most common use of dogs is in the safety assessment of new medicines for human or veterinary use as a second species following testing in rodents, in accordance with the regulations set out in the International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use. One of the most significant advancements in medical science involves the use of dogs in developing the answers to insulin production in the body for diabetics and the role of the pancreas in this process. They found that the pancreas was responsible for producing insulin in the body and that removal of the pancreas, resulted in the development of diabetes in the dog. After re-injecting the pancreatic extract (insulin), the blood glucose levels were significantly lowered. The advancements made in this research involving the use of dogs has resulted in a definite improvement in the quality of life for both humans and animals."]], "0_corpus": "wikipedia", "1_conv_id": "1eb6f036f8df4901a91c18d14c23452d", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "What animals are legally approved to be used for testing drugs?", "1_output": [["What animals are legally approved to be used for testing drugs?", "Animal testing\n\nAnimal testing is widely used to research human disease when human experimentation would be unfeasible or unethical. This strategy is made possible by the common descent of all living organisms, and the conservation of metabolic and developmental pathways and genetic material over the course of evolution. Performing experiments in model organisms allows for better understanding the disease process without the added risk of harming an actual human. The species of the model organism is usually chosen so that it reacts to disease or its treatment in a way that resembles human physiology as needed. Biological activity in a model organism does not ensure an effect in humans, and care must be taken when generalizing from one organism to another. However, many drugs, treatments and cures for human diseases are developed in part with the guidance of animal models. Treatments for animal diseases have also been developed, including for rabies, anthrax, glanders, feline immunodeficiency virus (FIV), tuberculosis, Texas cattle fever, classical swine fever (hog cholera), heartworm, and other parasitic infections. Animal experimentation continues to be required for biomedical research, and is used with the aim of solving medical problems such as Alzheimer's disease, AIDS, multiple sclerosis, spinal cord injury, many headaches, and other conditions in which there is no useful in vitro model system available.\nThe annual use of vertebrate animals—from zebrafish to non-human primates—was estimated at 192 million as of 2015. In the European Union, vertebrate species represent 93% of animals used in research, and 11.5 million animals were used there in 2011. The mouse (Mus musculus) is associated with many important biological discoveries of the 20th and 21st centuries, and by one estimate, the number of mice and rats used in the United States alone in 2001 was 80 million. In 2013, it was reported that mammals (mice and rats), fish, amphibians, and reptiles together accounted for over 85% of research animals. In 2022, a law was passed in the United States that eliminated the FDA requirement that all drugs be tested on animals."]], "1_corpus": "wikipedia"}
+{"tstamp": 1722521891.8626, "task_type": "retrieval", "type": "leftvote", "models": ["nomic-ai/nomic-embed-text-v1.5", "Salesforce/SFR-Embedding-2_R"], "ip": "", "0_conv_id": "6b01d2cbf4774ea2a4396a4d4a070129", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": "What animals are legally approved to be used for testing drugs?", "0_output": [["What animals are legally approved to be used for testing drugs?", "Animal testing\n\nDogs Dogs are widely used in biomedical research, testing, and education\u2014particularly beagles, because they are gentle and easy to handle, and to allow for comparisons with historical data from beagles (a Reduction technique). They are used as models for human and veterinary diseases in cardiology, endocrinology, and bone and joint studies, research that tends to be highly invasive, according to the Humane Society of the United States. The most common use of dogs is in the safety assessment of new medicines for human or veterinary use as a second species following testing in rodents, in accordance with the regulations set out in the International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use. One of the most significant advancements in medical science involves the use of dogs in developing the answers to insulin production in the body for diabetics and the role of the pancreas in this process. They found that the pancreas was responsible for producing insulin in the body and that removal of the pancreas, resulted in the development of diabetes in the dog. After re-injecting the pancreatic extract (insulin), the blood glucose levels were significantly lowered. The advancements made in this research involving the use of dogs has resulted in a definite improvement in the quality of life for both humans and animals."]], "0_corpus": "wikipedia", "1_conv_id": "1eb6f036f8df4901a91c18d14c23452d", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "What animals are legally approved to be used for testing drugs?", "1_output": [["What animals are legally approved to be used for testing drugs?", "Animal testing\n\nAnimal testing is widely used to research human disease when human experimentation would be unfeasible or unethical. This strategy is made possible by the common descent of all living organisms, and the conservation of metabolic and developmental pathways and genetic material over the course of evolution. Performing experiments in model organisms allows for better understanding the disease process without the added risk of harming an actual human. The species of the model organism is usually chosen so that it reacts to disease or its treatment in a way that resembles human physiology as needed. Biological activity in a model organism does not ensure an effect in humans, and care must be taken when generalizing from one organism to another. However, many drugs, treatments and cures for human diseases are developed in part with the guidance of animal models. Treatments for animal diseases have also been developed, including for rabies, anthrax, glanders, feline immunodeficiency virus (FIV), tuberculosis, Texas cattle fever, classical swine fever (hog cholera), heartworm, and other parasitic infections. Animal experimentation continues to be required for biomedical research, and is used with the aim of solving medical problems such as Alzheimer's disease, AIDS, multiple sclerosis, spinal cord injury, many headaches, and other conditions in which there is no useful in vitro model system available.\nThe annual use of vertebrate animals\u2014from zebrafish to non-human primates\u2014was estimated at 192 million as of 2015. In the European Union, vertebrate species represent 93% of animals used in research, and 11.5 million animals were used there in 2011. The mouse (Mus musculus) is associated with many important biological discoveries of the 20th and 21st centuries, and by one estimate, the number of mice and rats used in the United States alone in 2001 was 80 million. In 2013, it was reported that mammals (mice and rats), fish, amphibians, and reptiles together accounted for over 85% of research animals. In 2022, a law was passed in the United States that eliminated the FDA requirement that all drugs be tested on animals."]], "1_corpus": "wikipedia"}

data/retrieval_side_by_side-81269da8-6dc7-417a-8e0a-245c4a753482.jsonl

@@ -1,2 +1 @@
-{"tstamp": 1722386001.9562, "task_type": "retrieval", "type": "tievote", "models": ["Alibaba-NLP/gte-Qwen2-7B-instruct", "BAAI/bge-large-en-v1.5"], "ip": "", "0_conv_id": "2987867b0d714ee2b3991f314601f180", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": "how to write a good abstract", "0_output": [["how to write a good abstract", "Abstract (summary)\n\nThe use of abstracts to summarise science originates in the early 1800s, when the secretary of the Royal Society would record brief summaries of talks into the minutes of each meeting, which were referred to as 'abstracts'. The Royal Society abstracts from 1800 \u2013 1837 were later collated and published in the society's journal Philosophical Transactions, with the first group appearing in 1832. These abstracts were generally one or more pages long. Other learned societies adopted similar practices. The Royal Astronomical Society (RAS) may have been the first to publish its abstracts: the Monthly Notices of the RAS launched in 1827, containing (among other things) abstracts of talks given at their monthly meetings; the full papers were published months or years later in the Memoirs of the RAS. The RAS abstracts were between one and three paragraphs long. In both cases, these early abstracts were written by the learned society, not the author of the paper. Perhaps the earliest example of an abstract published alongside the paper it summarises was the 1919 paper On the Irregularities of Motion of the Foucault Pendulum published in the Physical Review of the American Physical Society, which often published abstracts thereafter.\nCopyright\nAbstracts are protected under copyright law just as any other form of written speech is protected.\nStructure\nAbstract is often expected to tell a complete story of the paper, as for most readers, abstract is the only part of the paper that will be read. It should allow the reader to give an elevator pitch of the full paper.\nAn academic abstract typically outlines four elements relevant to the completed work:\nThe research focus (statement of the problem(s)/specific gap in existing research/research issue(s) addressed);\nThe research methods (experimental research, case studies,  questionnaires, etc) used to solve the problem;\nThe major results/findings of the research; and\nThe main conclusions and recommendations (i.e., how the work answers the proposed research problem).\nIt may also contain brief references, although some publications' standard style omits references from the abstract, reserving them for the article body (which, by definition, treats the same topics but in more depth)."]], "0_corpus": "wikipedia", "1_conv_id": "13aca6a147054459a7ad0f23455c0359", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "how to write a good abstract", "1_output": [["how to write a good abstract", "Abstract (summary)\n\nAn abstract is a brief summary of a research article, thesis, review, conference proceeding, or any in-depth analysis of a particular subject and is often used to help the reader quickly ascertain the paper's purpose. When used, an abstract always appears at the beginning of a manuscript or typescript, acting as the point-of-entry for any given academic paper or patent application.  Abstracting and indexing services for various academic disciplines are aimed at compiling a body of literature for that particular subject."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722391832.6707, "task_type": "retrieval", "type": "tievote", "models": ["GritLM/GritLM-7B", "BAAI/bge-large-en-v1.5"], "ip": "", "0_conv_id": "39e382d9a54846878022c80cf55a9a6a", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe test was introduced by Turing in his 1950 paper \"Computing Machinery and Intelligence\" while working at the University of Manchester. It opens with the words: \"I propose to consider the question, 'Can machines think? Because \"thinking\" is difficult to define, Turing chooses to \"replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\" Turing describes the new form of the problem in terms of a three-person game called the \"imitation game\", in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: \"Are there imaginable digital computers which would do well in the imitation game?\" This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that \"machines can think\".\nSince Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a \"mind\", \"understanding\", or \"consciousness\", regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.\nHistory"]], "0_corpus": "wikipedia", "1_conv_id": "7b9ad82677324bd3a868102ae49d156f", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "Which test was devised to determine whether robots can think?", "1_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "1_corpus": "wikipedia"}

data/retrieval_side_by_side-c4bdd700-2c9c-4d9b-92d7-63fecd0c1575.jsonl

@@ -1,2 +1,2 @@
 {"tstamp": 1722438745.9725, "task_type": "retrieval", "type": "bothbadvote", "models": ["GritLM/GritLM-7B", "BAAI/bge-large-en-v1.5"], "ip": "", "0_conv_id": "e040b88a55984093915e318b32722ba4", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "what's the best way to earn gems in leafblower revolution", "0_output": [["what's the best way to earn gems in leafblower revolution", "Diablo Immortal\n\nOther players on services such as Twitter and Reddit later revised these estimates to between  and , due to the fact that reaching maximum levels of a legendary gem unlocks additional gem slots within items.Video Games Chronicle reported on the experience of YouTuber Raxxanterax, who noted he had easily lost to a \"whale\" player, while PCGamesN documented the experience of another YouTuber, Wudijo, who had become powerful enough to survive the toughest battles on their server without spending any money (although they did note that not all members of the victorious clan were free-to-play).Later reports also documented the experience of a player who spent \"around $100k\" on the game and achieved a battleground record too high for the game's matchmaking system to pair them with any other players, thereby preventing them from playing that game mode. Additional negative sentiment was directed at the discovery of hidden caps for in-game rewards, which the game does not make explicit to players. Such caps include a daily limit on the number of \"legendary\" level items dropped, and on the number of rewards available from side-quests and random events. "]], "0_corpus": "wikipedia", "1_conv_id": "0efb8fb1f46c464984c1bea286561147", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "what's the best way to earn gems in leafblower revolution", "1_output": [["what's the best way to earn gems in leafblower revolution", "Grinding (video games)\n\nMinecraft allows players to gather materials by exploring in caves, on the surface, and by other means. However, some materials are harder to find than others, such as diamond or netherite. The most common strategy for finding these materials is to obtain the fastest tools possible, that being a diamond or netherite pickaxe, shovel and axe (depending on the material that you want to grind for) that has been enchanted with Efficiency V and using this pickaxe in tandem with a Haste beacon. With this combination, most common blocks can be mined immediately, massively decreasing the time it takes to find rare materials.\nAdvanced rewards\nThe Lord of the Rings Online features a \"title system\" in which players are awarded special titles, and often new abilities, for killing large quantities of specific types of enemies. This can make grinding worthwhile, as the player benefits from the added experience points and can receive a title they can show off to other players. For example, killing a large number of Wargs grants the player the \"Warg-Slayer\" title. Killing even more Wargs results in more advanced titles, such as \"Warg Foe\". This system also existed in City of Heroes/City of Villains, where these titles were named \"badges\".\nFinal Fantasy XII features a \"Chain\" effect, which occurs when a player repeatedly kills the same monster; doing so increases the number in the Chain and improves the quality of the items dropped by the killed enemy. With a maximum of 999 chained kills, the Chain can only be broken by killing a different monster or leaving the area."]], "1_corpus": "wikipedia"}
-{"tstamp": 1722439112.2598, "task_type": "retrieval", "type": "tievote", "models": ["BM25", "voyage-multilingual-2"], "ip": "", "0_conv_id": "a38e1f72a3ea4744a5cbd4d35c13be33", "0_model_name": "BM25", "0_prompt": "where was ismail kadare born?", "0_output": [["where was ismail kadare born?", "Ismail Kadare\n\nEarly life and education Ismail Kadare was born on 28 January 1936, in the Kingdom of Albania during the reign of King Zog I. He was born in Gjirokastër, an historic Ottoman Empire fortress mountain city of tall stone houses in southern Albania, a dozen miles from the border with Greece. He lived there on a crooked, narrow street known as Lunatics' Lane.\nIsmail's parents were Halit Kadare, a post office employee, and Hatixhe Dobi, a homemaker, who had married in 1933 when she was 17. On his mother's side, his great-grandfather was a Bejtexhi of the Bektashi Order, known as Hoxhë Dobi. Though he was born into a Muslim family, he was an atheist.\nThree years after Kadare was born, Italian Prime Minister Benito Mussolini's troops invaded Albania and ousted the king. Italian rule followed. He was nine years old when the Italian troops were withdrawn, and the communist-led People's Socialist Republic of Albania was established.\nKadare attended primary and secondary schools in Gjirokastër. He then studied Languages and Literature at the Faculty of History and Philology of the University of Tirana. In 1956, he received a teacher's diploma. He lived in Tirana until moving to France in 1990.\nLiterary career\nEarly\nAt age 11, Kadare read William Shakespeare's play Macbeth. He recalled years later: \"Because I did not yet understand that I could simply purchase it in a bookstore, I copied much of it by hand and took it home. My childhood imagination pushed me to feel like a co-author of the play.\""]], "0_corpus": "wikipedia", "1_conv_id": "9d2a4cd41334473993058b7df7e28284", "1_model_name": "voyage-multilingual-2", "1_prompt": "where was ismail kadare born?", "1_output": [["where was ismail kadare born?", "Ismail Kadare\n\nEarly life and education Ismail Kadare was born on 28 January 1936, in the Kingdom of Albania during the reign of King Zog I. He was born in Gjirokastër, an historic Ottoman Empire fortress mountain city of tall stone houses in southern Albania, a dozen miles from the border with Greece. He lived there on a crooked, narrow street known as Lunatics' Lane.\nIsmail's parents were Halit Kadare, a post office employee, and Hatixhe Dobi, a homemaker, who had married in 1933 when she was 17. On his mother's side, his great-grandfather was a Bejtexhi of the Bektashi Order, known as Hoxhë Dobi. Though he was born into a Muslim family, he was an atheist.\nThree years after Kadare was born, Italian Prime Minister Benito Mussolini's troops invaded Albania and ousted the king. Italian rule followed. He was nine years old when the Italian troops were withdrawn, and the communist-led People's Socialist Republic of Albania was established.\nKadare attended primary and secondary schools in Gjirokastër. He then studied Languages and Literature at the Faculty of History and Philology of the University of Tirana. In 1956, he received a teacher's diploma. He lived in Tirana until moving to France in 1990.\nLiterary career\nEarly\nAt age 11, Kadare read William Shakespeare's play Macbeth. He recalled years later: \"Because I did not yet understand that I could simply purchase it in a bookstore, I copied much of it by hand and took it home. My childhood imagination pushed me to feel like a co-author of the play.\""]], "1_corpus": "wikipedia"}
+{"tstamp": 1722439112.2598, "task_type": "retrieval", "type": "tievote", "models": ["BM25", "voyage-multilingual-2"], "ip": "", "0_conv_id": "a38e1f72a3ea4744a5cbd4d35c13be33", "0_model_name": "BM25", "0_prompt": "where was ismail kadare born?", "0_output": [["where was ismail kadare born?", "Ismail Kadare\n\nEarly life and education Ismail Kadare was born on 28 January 1936, in the Kingdom of Albania during the reign of King Zog I. He was born in Gjirokast\u00ebr, an historic Ottoman Empire fortress mountain city of tall stone houses in southern Albania, a dozen miles from the border with Greece. He lived there on a crooked, narrow street known as Lunatics' Lane.\nIsmail's parents were Halit Kadare, a post office employee, and Hatixhe Dobi, a homemaker, who had married in 1933 when she was 17. On his mother's side, his great-grandfather was a Bejtexhi of the Bektashi Order, known as Hoxh\u00eb Dobi. Though he was born into a Muslim family, he was an atheist.\nThree years after Kadare was born, Italian Prime Minister Benito Mussolini's troops invaded Albania and ousted the king. Italian rule followed. He was nine years old when the Italian troops were withdrawn, and the communist-led People's Socialist Republic of Albania was established.\nKadare attended primary and secondary schools in Gjirokast\u00ebr. He then studied Languages and Literature at the Faculty of History and Philology of the University of Tirana. In 1956, he received a teacher's diploma. He lived in Tirana until moving to France in 1990.\nLiterary career\nEarly\nAt age 11, Kadare read William Shakespeare's play Macbeth. He recalled years later: \"Because I did not yet understand that I could simply purchase it in a bookstore, I copied much of it by hand and took it home. My childhood imagination pushed me to feel like a co-author of the play.\""]], "0_corpus": "wikipedia", "1_conv_id": "9d2a4cd41334473993058b7df7e28284", "1_model_name": "voyage-multilingual-2", "1_prompt": "where was ismail kadare born?", "1_output": [["where was ismail kadare born?", "Ismail Kadare\n\nEarly life and education Ismail Kadare was born on 28 January 1936, in the Kingdom of Albania during the reign of King Zog I. He was born in Gjirokast\u00ebr, an historic Ottoman Empire fortress mountain city of tall stone houses in southern Albania, a dozen miles from the border with Greece. He lived there on a crooked, narrow street known as Lunatics' Lane.\nIsmail's parents were Halit Kadare, a post office employee, and Hatixhe Dobi, a homemaker, who had married in 1933 when she was 17. On his mother's side, his great-grandfather was a Bejtexhi of the Bektashi Order, known as Hoxh\u00eb Dobi. Though he was born into a Muslim family, he was an atheist.\nThree years after Kadare was born, Italian Prime Minister Benito Mussolini's troops invaded Albania and ousted the king. Italian rule followed. He was nine years old when the Italian troops were withdrawn, and the communist-led People's Socialist Republic of Albania was established.\nKadare attended primary and secondary schools in Gjirokast\u00ebr. He then studied Languages and Literature at the Faculty of History and Philology of the University of Tirana. In 1956, he received a teacher's diploma. He lived in Tirana until moving to France in 1990.\nLiterary career\nEarly\nAt age 11, Kadare read William Shakespeare's play Macbeth. He recalled years later: \"Because I did not yet understand that I could simply purchase it in a bookstore, I copied much of it by hand and took it home. My childhood imagination pushed me to feel like a co-author of the play.\""]], "1_corpus": "wikipedia"}

data/retrieval_side_by_side-f62bcad9-19c4-430b-a7ba-aa2bfeba99dc.jsonl

@@ -1,5 +1,3 @@
-{"tstamp": 1722405190.7377, "task_type": "retrieval", "type": "tievote", "models": ["Alibaba-NLP/gte-Qwen2-7B-instruct", "BAAI/bge-large-en-v1.5"], "ip": "", "0_conv_id": "11258040188c42fbb0b15638d9f6dd84", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": "How to make cake?", "0_output": [["How to make cake?", "Cake\n\nCake is a flour confection made from flour, sugar, and other ingredients and is usually baked. In their oldest forms, cakes were modifications of bread, but cakes now cover a wide range of preparations that can be simple or elaborate and which share features with desserts such as pastries, meringues, custards, and pies.\nThe most common ingredients include flour, sugar, eggs, fat (such as butter, oil, or margarine), a liquid, and a leavening agent, such as baking soda or baking powder. Common additional ingredients include dried, candied, or fresh fruit, nuts, cocoa, and extracts such as vanilla, with numerous substitutions for the primary ingredients. Cakes can also be filled with fruit preserves, nuts, or dessert sauces (like custard, jelly, cooked fruit, whipped cream, or syrups), iced with buttercream or other icings, and decorated with marzipan, piped borders, or candied fruit.\nCake is often served as a celebratory dish on ceremonial occasions, such as weddings, anniversaries, and birthdays. There are countless cake recipes; some are bread-like, some are rich and elaborate, and many are centuries old. Cake making is no longer a complicated procedure; while at one time considerable labor went into cake making (particularly the whisking of egg foams), baking equipment and directions have been simplified so that even the most amateur of cooks may bake a cake."]], "0_corpus": "wikipedia", "1_conv_id": "b198fea204d3473292df16a104e1ed81", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "How to make cake?", "1_output": [["How to make cake?", "Cake\n\nCake is a flour confection made from flour, sugar, and other ingredients and is usually baked. In their oldest forms, cakes were modifications of bread, but cakes now cover a wide range of preparations that can be simple or elaborate and which share features with desserts such as pastries, meringues, custards, and pies.\nThe most common ingredients include flour, sugar, eggs, fat (such as butter, oil, or margarine), a liquid, and a leavening agent, such as baking soda or baking powder. Common additional ingredients include dried, candied, or fresh fruit, nuts, cocoa, and extracts such as vanilla, with numerous substitutions for the primary ingredients. Cakes can also be filled with fruit preserves, nuts, or dessert sauces (like custard, jelly, cooked fruit, whipped cream, or syrups), iced with buttercream or other icings, and decorated with marzipan, piped borders, or candied fruit.\nCake is often served as a celebratory dish on ceremonial occasions, such as weddings, anniversaries, and birthdays. There are countless cake recipes; some are bread-like, some are rich and elaborate, and many are centuries old. Cake making is no longer a complicated procedure; while at one time considerable labor went into cake making (particularly the whisking of egg foams), baking equipment and directions have been simplified so that even the most amateur of cooks may bake a cake."]], "1_corpus": "wikipedia"}
-{"tstamp": 1722405395.3689, "task_type": "retrieval", "type": "rightvote", "models": ["Alibaba-NLP/gte-Qwen2-7B-instruct", "BAAI/bge-large-en-v1.5"], "ip": "", "0_conv_id": "5eb5421a209846b8aab73cddb8a8c999", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": "Scene detection from video", "0_output": [["Scene detection from video", "Title: Dealing with sequences in the RGBDT space\n\nAbstract: Most of the current research in computer vision is focused on working with single images without taking in account temporal information. We present a probabilistic non-parametric model that mixes multiple information cues from devices to segment regions that contain moving objects in image sequences. We prepared an experimental setup to show the importance of using previous information for obtaining an accurate segmentation result, using a novel dataset that provides sequences in the RGBDT space. We label the detected regions ts with a state-of-the-art human detector. Each one of the detected regions is at least marked as human once."]], "0_corpus": "arxiv", "1_conv_id": "39376a868a284ddebf65e0e4719b5d4c", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "Scene detection from video", "1_output": [["Scene detection from video", "Title: Learnable Optimal Sequential Grouping for Video Scene Detection\n\nAbstract: Video scene detection is the task of dividing videos into temporal semantic chapters. This is an important preliminary step before attempting to analyze heterogeneous video content. Recently, Optimal Sequential Grouping (OSG) was proposed as a powerful unsupervised solution to solve a formulation of the video scene detection problem. In this work, we extend the capabilities of OSG to the learning regime. By giving the capability to both learn from examples and leverage a robust optimization formulation, we can boost performance and enhance the versatility of the technology. We present a comprehensive analysis of incorporating OSG into deep learning neural networks under various configurations. These configurations include learning an embedding in a straight-forward manner, a tailored loss designed to guide the solution of OSG, and an integrated model where the learning is performed through the OSG pipeline. With thorough evaluation and analysis, we assess the benefits and behavior of the various configurations, and show that our learnable OSG approach exhibits desirable behavior and enhanced performance compared to the state of the art."]], "1_corpus": "arxiv"}
 {"tstamp": 1722410176.2348, "task_type": "retrieval", "type": "tievote", "models": ["GritLM/GritLM-7B", "BAAI/bge-large-en-v1.5"], "ip": "", "0_conv_id": "f7cba7820d484a6e9a535e25f2e59795", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "what is a cuticle", "0_output": [["what is a cuticle", "Cuticle\n\nA cuticle (), or cuticula, is any of a variety of tough but flexible, non-mineral outer coverings of an organism, or parts of an organism, that provide protection. Various types of \"cuticle\" are non-homologous, differing in their origin, structure, function, and chemical composition.\nHuman anatomy\nIn human anatomy, \"cuticle\" can refer to several structures, but it is used in general parlance, and even by medical professionals, to refer to the thickened layer of skin surrounding fingernails and toenails (the eponychium), and to refer to the superficial layer of overlapping cells covering the hair shaft (cuticula pili), consisting of dead cells, that locks the hair into its follicle. It can also be used as a synonym for the epidermis, the outer layer of skin.\nCuticle of invertebrates\nIn zoology, the invertebrate cuticle or cuticula is a multi-layered structure outside the epidermis of many invertebrates, notably arthropods and roundworms, in which it forms an exoskeleton (see arthropod exoskeleton).\nThe main structural components of the nematode cuticle are proteins, highly cross-linked collagens and specialised insoluble proteins known as \"cuticlins\", together with glycoproteins and lipids."]], "0_corpus": "wikipedia", "1_conv_id": "502b1819fb7448ee91f9932a135d2bc2", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "what is a cuticle", "1_output": [["what is a cuticle", "Cuticle\n\nA cuticle (), or cuticula, is any of a variety of tough but flexible, non-mineral outer coverings of an organism, or parts of an organism, that provide protection. Various types of \"cuticle\" are non-homologous, differing in their origin, structure, function, and chemical composition.\nHuman anatomy\nIn human anatomy, \"cuticle\" can refer to several structures, but it is used in general parlance, and even by medical professionals, to refer to the thickened layer of skin surrounding fingernails and toenails (the eponychium), and to refer to the superficial layer of overlapping cells covering the hair shaft (cuticula pili), consisting of dead cells, that locks the hair into its follicle. It can also be used as a synonym for the epidermis, the outer layer of skin.\nCuticle of invertebrates\nIn zoology, the invertebrate cuticle or cuticula is a multi-layered structure outside the epidermis of many invertebrates, notably arthropods and roundworms, in which it forms an exoskeleton (see arthropod exoskeleton).\nThe main structural components of the nematode cuticle are proteins, highly cross-linked collagens and specialised insoluble proteins known as \"cuticlins\", together with glycoproteins and lipids."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722413597.7439, "task_type": "retrieval", "type": "rightvote", "models": ["jinaai/jina-embeddings-v2-base-en", "voyage-multilingual-2"], "ip": "", "0_conv_id": "c46babe33e3c40fba0b1cc820ca8384a", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "Cognitive test\n\nCognitive tests are assessments of the cognitive capabilities of humans and other animals. Tests administered to humans include various forms of IQ tests; those administered to animals include the mirror test (a test of visual self-awareness) and the T maze test (which tests learning ability). Such testing is used in psychology and psychometrics, as well as other fields studying human and animal intelligence."]], "0_corpus": "wikipedia", "1_conv_id": "b5e90ef4f21148029ef2fc7495e295fb", "1_model_name": "BM25", "1_prompt": "Which test was devised to determine whether robots can think?", "1_output": [["Which test was devised to determine whether robots can think?", "Artificial intelligence\n\nIn the late teens and early 2020s, AGI companies began to deliver programs that created enormous interest. In 2015, AlphaGo, developed by DeepMind, beat the world champion Go player. The program was taught only the rules of the game and developed strategy by itself. GPT-3 is a large language model that was released in 2020 by OpenAI and is capable of generating high-quality human-like text. These programs, and others, inspired an aggressive AI boom, where large companies began investing billions in AI research. According to AI Impacts, about $50 billion annually was invested in \"AI\" around 2022 in the U.S. alone and about 20% of the new U.S. Computer Science PhD graduates have specialized in \"AI\".\nAbout 800,000 \"AI\"-related U.S. job openings existed in 2022.\nPhilosophy\nDefining artificial intelligence\nAlan Turing wrote in 1950 \"I propose to consider the question 'can machines think'?\" He advised changing the question from whether a machine \"thinks\", to \"whether or not it is possible for machinery to show intelligent behaviour\". He devised the Turing test, which measures the ability of a machine to simulate human conversation. Since we can only observe the behavior of the machine, it does not matter if it is \"actually\" thinking or literally has a \"mind\". Turing notes that we can not determine these things about other people but \"it is usual to have a polite convention that everyone thinks.\""]], "1_corpus": "wikipedia"}
 {"tstamp": 1722432375.9194, "task_type": "retrieval", "type": "tievote", "models": ["jinaai/jina-embeddings-v2-base-en", "BAAI/bge-large-en-v1.5"], "ip": "", "0_conv_id": "16bc3d67d1af469a9cc0ee66d974af5a", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "What is cosine similarity?", "0_output": [["What is cosine similarity?", "Cosine similarity\n\nIn data analysis, cosine similarity is a measure of similarity between two non-zero vectors defined in an inner product space. Cosine similarity is the cosine of the angle between the vectors; that is, it is the dot product of the vectors divided by the product of their lengths. It follows that the cosine similarity does not depend on the magnitudes of the vectors, but only on their angle. The cosine similarity always belongs to the interval  For example, two proportional vectors have a cosine similarity of 1, two orthogonal vectors have a similarity of 0, and two opposite vectors have a similarity of -1. In some contexts, the component values of the vectors cannot be negative, in which case the cosine similarity is bounded in .\nFor example, in information retrieval and text mining, each word is assigned a different coordinate and a document is represented by the vector of the numbers of occurrences of each word in the document. Cosine similarity then gives a useful measure of how similar two documents are likely to be, in terms of their subject matter, and independently of the length of the documents.\nThe technique is also used to measure cohesion within clusters in the field of data mining.\nOne advantage of cosine similarity is its low complexity, especially for sparse vectors: only the non-zero coordinates need to be considered.\nOther names for cosine similarity include Orchini similarity and Tucker coefficient of congruence; the Otsuka\u2013Ochiai similarity (see below) is cosine similarity applied to binary data."]], "0_corpus": "wikipedia", "1_conv_id": "2ee5241754c8451bb390c3a4c4064166", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "What is cosine similarity?", "1_output": [["What is cosine similarity?", "Cosine similarity\n\nIn data analysis, cosine similarity is a measure of similarity between two non-zero vectors defined in an inner product space. Cosine similarity is the cosine of the angle between the vectors; that is, it is the dot product of the vectors divided by the product of their lengths. It follows that the cosine similarity does not depend on the magnitudes of the vectors, but only on their angle. The cosine similarity always belongs to the interval  For example, two proportional vectors have a cosine similarity of 1, two orthogonal vectors have a similarity of 0, and two opposite vectors have a similarity of -1. In some contexts, the component values of the vectors cannot be negative, in which case the cosine similarity is bounded in .\nFor example, in information retrieval and text mining, each word is assigned a different coordinate and a document is represented by the vector of the numbers of occurrences of each word in the document. Cosine similarity then gives a useful measure of how similar two documents are likely to be, in terms of their subject matter, and independently of the length of the documents.\nThe technique is also used to measure cohesion within clusters in the field of data mining.\nOne advantage of cosine similarity is its low complexity, especially for sparse vectors: only the non-zero coordinates need to be considered.\nOther names for cosine similarity include Orchini similarity and Tucker coefficient of congruence; the Otsuka\u2013Ochiai similarity (see below) is cosine similarity applied to binary data."]], "1_corpus": "wikipedia"}

data/retrieval_single_choice-928d45d9-19ab-4d05-9aae-4b6574886d0d.jsonl

@@ -1,2 +1,2 @@
 {"tstamp": 1722534819.4872, "task_type": "retrieval", "type": "upvote", "models": "Salesforce/SFR-Embedding-2_R", "ip": "", "conv_id": "41f5727bbcf14727a17f7bb2cfe2a583", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "Has Peter Scholze won a fields medal?", "output": [["Has Peter Scholze won a fields medal?", "Peter Scholze\n\nPeter Scholze (; born 11 December 1987) is a German mathematician known for his work in arithmetic geometry. He has been a professor at the University of Bonn since 2012 and director at the Max Planck Institute for Mathematics since 2018. He has been called one of the leading mathematicians in the world. He won the Fields Medal in 2018, which is regarded as the highest professional honor in mathematics.\nEarly life and education\nScholze was born in Dresden and grew up in Berlin. His father is a physicist, his mother a computer scientist, and his sister studied chemistry. He attended the  in Berlin-Friedrichshain, a gymnasium devoted to  mathematics and science. As a student, Scholze participated in the International Mathematical Olympiad, winning three gold medals and one silver medal.\nHe studied at the University of Bonn and completed his bachelor's degree in three semesters and his master's degree in two further semesters. He obtained his Ph.D. in 2012 under the supervision of Michael Rapoport."]], "corpus": "wikipedia"}
-{"tstamp": 1722535220.7779, "task_type": "retrieval", "type": "upvote", "models": "Salesforce/SFR-Embedding-2_R", "ip": "", "conv_id": "a1969f23d81b4c9e8692533efa567275", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "Major breakthroughs behind the current EV battery technology?", "output": [["Major breakthroughs behind the current EV battery technology?", "Electric vehicle battery\n\nAn electric vehicle battery is a rechargeable battery used to power the electric motors of a battery electric vehicle (BEV) or hybrid electric vehicle (HEV).\nThey are typically lithium-ion batteries that are designed for high power-to-weight ratio and energy density. Compared to liquid fuels, most current battery technologies have much lower specific energy. This increases the weight of vehicles or reduces their range.\nLi-NMC batteries using lithium nickel manganese cobalt oxides are the most common in EV. The lithium iron phosphate battery (LFP) is on the rise, reaching 41 % global market share by capacity for BEVs in 2023. LFP batteries are heavier but cheaper and more sustainable. At the same time, the first commercial passenger cars are using a sodium-ion battery (Na-ion) completely avoiding the need for critical minerals."]], "corpus": "wikipedia"}
+{"tstamp": 1722535220.7779, "task_type": "retrieval", "type": "upvote", "models": "Salesforce/SFR-Embedding-2_R", "ip": "", "conv_id": "a1969f23d81b4c9e8692533efa567275", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "Major breakthroughs behind the current EV battery technology?", "output": [["Major breakthroughs behind the current EV battery technology?", "Electric vehicle battery\n\nAn electric vehicle battery is a rechargeable battery used to power the electric motors of a battery electric vehicle (BEV) or hybrid electric vehicle (HEV).\nThey are typically lithium-ion batteries that are designed for high power-to-weight ratio and energy density. Compared to liquid fuels, most current battery technologies have much lower specific energy. This increases the weight of vehicles or reduces their range.\nLi-NMC batteries using lithium nickel manganese cobalt oxides are the most common in EV. The lithium iron phosphate battery (LFP) is on the rise, reaching 41\u00a0% global market share by capacity for BEVs in 2023. LFP batteries are heavier but cheaper and more sustainable. At the same time, the first commercial passenger cars are using a sodium-ion battery (Na-ion) completely avoiding the need for critical minerals."]], "corpus": "wikipedia"}

data/sts_battle-0325b067-7cac-407c-9ffa-76d99f10c9f7.jsonl

@@ -1,4 +1,2 @@
-{"tstamp": 1722262971.6058, "task_type": "sts", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "562b48d88d3840b282ed0bf3c590341a", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_txt0": "hello", "0_txt1": "good morning", "0_txt2": "\u65e9\u4e0a\u597d", "0_output": "", "1_conv_id": "809eb6825e7d4713bc192f9b915fa4e1", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_txt0": "hello", "1_txt1": "good morning", "1_txt2": "\u65e9\u4e0a\u597d", "1_output": ""}
 {"tstamp": 1722263131.0162, "task_type": "sts", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "da247cb7f544491ea524047da28e0d8b", "0_model_name": "GritLM/GritLM-7B", "0_txt0": "People at an outdoor market", "0_txt1": "A black mother in an orange dress with her two little boys and husband at an outdoor market stand.", "0_txt2": "A family skeet shooting", "0_output": "", "1_conv_id": "9f7c97678ebf44e7814b60495025cff7", "1_model_name": "intfloat/multilingual-e5-large-instruct", "1_txt0": "People at an outdoor market", "1_txt1": "A black mother in an orange dress with her two little boys and husband at an outdoor market stand.", "1_txt2": "A family skeet shooting", "1_output": ""}
 {"tstamp": 1722263165.1325, "task_type": "sts", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "644c59f0578b48068f1228870ba757b0", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_txt0": "The dog likes to catch baseballs.", "0_txt1": "a puppy about to jump to intercept a yellow ball", "0_txt2": "The dog is trying to catch a tennis ball.", "0_output": "", "1_conv_id": "43f756f9250e4c2f8f7bd1eaffe3eaf2", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_txt0": "The dog likes to catch baseballs.", "1_txt1": "a puppy about to jump to intercept a yellow ball", "1_txt2": "The dog is trying to catch a tennis ball.", "1_output": ""}
-{"tstamp": 1722263197.3786, "task_type": "sts", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "1be218a193ec45689faeb8ff9318688a", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_txt0": "People are shopping.", "0_txt1": "Numerous customers browsing for produce in a market", "0_txt2": "People are showering.", "0_output": "", "1_conv_id": "57ea514b971f492da35c07bdcd7dd4aa", "1_model_name": "BAAI/bge-large-en-v1.5", "1_txt0": "People are shopping.", "1_txt1": "Numerous customers browsing for produce in a market", "1_txt2": "People are showering.", "1_output": ""}