{"tstamp": 1722304978.8845, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722304977.8763, "finish": 1722304978.8845, "ip": "", "conv_id": "559be19d148045bd97deb6c2be8ad7ee", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "Research on modifying sentence sentiment without altering the core content through a two-step masking and infilling approach.", "output": [["Research on modifying sentence sentiment without altering the core content through a two-step masking and infilling approach.", "Title: \"Mask and Infill\" : Applying Masked Language Model to Sentiment Transfer\n\nAbstract: This paper focuses on the task of sentiment transfer on non-parallel text, which modifies sentiment attributes (e.g., positive or negative) of sentences while preserving their attribute-independent content. Due to the limited capability of RNNbased encoder-decoder structure to capture deep and long-range dependencies among words, previous works can hardly generate satisfactory sentences from scratch. When humans convert the sentiment attribute of a sentence, a simple but effective approach is to only replace the original sentimental tokens in the sentence with target sentimental expressions, instead of building a new sentence from scratch. Such a process is very similar to the task of Text Infilling or Cloze, which could be handled by a deep bidirectional Masked Language Model (e.g. BERT). So we propose a two step approach \"Mask and Infill\". In the mask step, we separate style from content by masking the positions of sentimental tokens. In the infill step, we retrofit MLM to Attribute Conditional MLM, to infill the masked positions by predicting words or phrases conditioned on the context1 and target sentiment. We evaluate our model on two review datasets with quantitative, qualitative, and human evaluations. Experimental results demonstrate that our models improve state-of-the-art performance."]], "corpus": "arxiv"}
{"tstamp": 1722304978.8845, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722304977.8763, "finish": 1722304978.8845, "ip": "", "conv_id": "71e05d6cfbba477b9531770801bfdb37", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "Research on modifying sentence sentiment without altering the core content through a two-step masking and infilling approach.", "output": [["Research on modifying sentence sentiment without altering the core content through a two-step masking and infilling approach.", "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": 1722304986.0474, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722304985.1247, "finish": 1722304986.0474, "ip": "", "conv_id": "8307fc5bf26b463e864d9ff494a6ca1a", "model_name": "GritLM/GritLM-7B", "prompt": "who sings baby it's cold outside original", "output": [["who sings baby it's cold outside original", "Baby, It's Cold Outside\n\nDinah Shore and Buddy Clark with Ted Dale and his orchestra; recorded on March 17 and released by Columbia Records (peaked at No. 3 on Billboard Records Most Played By Disk Jockeys chart, at No. 4 on Billboard Best-Selling Popular Retail Records chart, and No. 6 on Billboard Most-Played Juke Box Records chart in mid-1949)\nMargaret Whiting and Johnny Mercer with Paul Weston and his orchestra; recorded on March 18 and released by Capitol Records (peaked at No. 3 on Billboard Records Most Played By Disk Jockeys chart, at No. 4 on Billboard Best-Selling Popular Retail Records chart [lasting 19 weeks on the chart], and No. 8 on Billboard Most-Played Juke Box Records chart in mid-1949)\nOther recordings\nAs of 2020, there are over 400 recordings of the song. The following list is incomplete.\nCharts\nDean Martin version\nRay Charles and Betty Carter version\nTom Jones and Cerys Matthews version\nElla Fitzgerald and Louis Jordan version\nGlee Cast version\nWillie Nelson and Norah Jones version\nShe & Him version\nLady Antebellum / Lady A version\nKelly Clarkson and Ronnie Dunn version\nIdina Menzel and Michael Bublé version\nBrett Eldredge and Meghan Trainor version\nJohn Legend and Kelly Clarkson version\nYear-end charts\nIdina Menzel and Michael Bublé version\nBrett Eldredge and Meghan Trainor version\nCertifications\nDean Martin version\nIdina Menzel and Michael Bublé version\nBrett Eldredge and Meghan Trainor version"]], "corpus": "wikipedia"}
{"tstamp": 1722304986.0474, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722304985.1247, "finish": 1722304986.0474, "ip": "", "conv_id": "4a6085badadb42d6880032af43e5dd2f", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "who sings baby it's cold outside original", "output": [["who sings baby it's cold outside original", "Baby, It's Cold Outside\n\n\"Baby, It's Cold Outside\" is a popular song written by Frank Loesser in 1944 and popularized in the 1949 film Neptune's Daughter. While the lyrics make no mention of a holiday, it is commonly regarded as a Christmas song owing to its winter theme.  The song was released in eight recordings in 1949—including well-known versions by Ella Fitzgerald and Louis Jordan, and by Dean Martin and Marilyn Maxwell—and has been covered numerous times since.\nHistory\nIn 1944, Loesser wrote \"Baby, It's Cold Outside\" to sing with his wife, Lynn Garland, at their housewarming party in New York City at the Navarro Hotel. They sang the song to indicate to guests that it was time to leave. Garland has written that after the first performance, \"We became instant parlor room stars. We got invited to all the best parties for years on the basis of 'Baby.' It was our ticket to caviar and truffles. Parties were built around our being the closing act.\" In 1948, after years of performing the song, Loesser sold it to MGM for the 1949 romantic comedy Neptune's Daughter. Garland was furious: \"I felt as betrayed as if I'd caught him in bed with another woman.\""]], "corpus": "wikipedia"}
{"tstamp": 1722304990.98, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722304990.7411, "finish": 1722304990.98, "ip": "", "conv_id": "c33f55221d004dba9557c88d0b09ce3d", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "are doughy cookies safe to eat?", "output": [["are doughy cookies safe to eat?", "Stackoverflow Stackexchange\n\nQ: pharo creating a global variable like transcript I want to create a variable named MameGap which is accessible from every where.\nI can print some words to transcript for ex. from a method of a class. I want to do this for MameGap too.\nI tried something like this \nMameGap:= MyClass new.\nSmalltalk at: #myMap put: MameGap.\n\nI want to access MameGap like this\ndoSomething: aVar\n   |x|\n   x:= MameGap getInt.\n   ^x*3\n\n\nA: You have to do:\nSmalltalk at: #MameGap put: MyClass new\n\nalso you can put there just a class object, like \n Smalltalk at: #MameGap put: MyClass\n\nand sen to it class-side messages\n"]], "corpus": "stackexchange"}
{"tstamp": 1722304990.98, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1722304990.7411, "finish": 1722304990.98, "ip": "", "conv_id": "15e46770ed5144848b667dc921b96344", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "are doughy cookies safe to eat?", "output": [["are doughy cookies safe to eat?", "Stackoverflow Stackexchange\n\nQ: Results from query as argument for WHERE statement in MySQL I have two tables: orders and orderProducts. They both have a column called 'order_id'.\norders has a column named 'date_created'\nordersProducts has a column named 'SKU'\nI want to SELECT SKUs in within a date range.\nMy query so far is:\nSELECT `SKU`\nFROM `orderProducts`\nINNER JOIN orders\nON orderproducts.order_id = orders.order_id\nWHERE orders.order_id in (SELECT id FROM orders WHERE date_created BETWEEN '2014-10-01' AND '2015-03-31' ORDER BY date_created DESC)\n\nThe query runs but it returns nothings. What am I missing here?\n\nA: Try putting date condition in the where clause, there is no need for the subquery:\nselect op.`SKU`\nfrom `orderProducts` op\njoin `orders` o using(`order_id`)\nwhere o.`date_created` between '2014-10-01' and '2015-03-31'\n\n"]], "corpus": "stackexchange"}
{"tstamp": 1722305000.0153, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722304999.8039, "finish": 1722305000.0153, "ip": "", "conv_id": "100375864a8944319e64d77e419c756d", "model_name": "GritLM/GritLM-7B", "prompt": "what emperor took over france after the reign of terror", "output": [["what emperor took over france after the reign of terror", "History of Europe\n\nOn 20 September 1792 the National Convention abolished the monarchy and declared France a republic. Due to the emergency of war, the National Convention created the Committee of Public Safety to act as the country's executive. Under Maximilien de Robespierre, the committee initiated the Reign of Terror, during which up to 40,000 people were executed in Paris, mainly nobles and those convicted by the Revolutionary Tribunal, often on the flimsiest of evidence. Internal tensions at Paris drove the Committee towards increasing assertions of radicalism and increasing suspicions. A few months into this phase, more and more prominent revolutionaries were being sent to the guillotine by Robespierre and his faction, for example Madame Roland and Georges Danton. Elsewhere in the country, counter-revolutionary insurrections were brutally suppressed. The regime was overthrown in the coup of 9 Thermidor (27 July 1794) and Robespierre was executed. The regime which followed ended the Terror and relaxed Robespierre's more extreme policies.\nNapoleon\nNapoleon Bonaparte was France's most successful general in the Revolutionary wars. In 1799 on 18 Brumaire (9 November) he overthrew the government, replacing it with the Consulate, which he dominated. He gained popularity in France by restoring the Church, keeping taxes low, centralizing power in Paris, and winning glory on the battlefield. In 1804 he crowned himself Emperor. In 1805, Napoleon planned to invade Britain, but a renewed British alliance with Russia and Austria (Third Coalition), forced him to turn his attention towards the continent, while at the same time the French fleet was demolished by the British at the Battle of Trafalgar, ending any plan to invade Britain. On 2 December 1805, Napoleon defeated a numerically superior Austro-Russian army at Austerlitz, forcing Austria's withdrawal from the coalition (see Treaty of Pressburg) and dissolving the Holy Roman Empire. In 1806, a Fourth Coalition was set up. On 14 October Napoleon defeated the Prussians at the Battle of Jena-Auerstedt, marched through Germany and defeated the Russians on 14 June 1807 at Friedland. The Treaties of Tilsit divided Europe between France and Russia and created the Duchy of Warsaw."]], "corpus": "wikipedia"}
{"tstamp": 1722305000.0153, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722304999.8039, "finish": 1722305000.0153, "ip": "", "conv_id": "bad28d9bdb47406f9cfd72c620633507", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "what emperor took over france after the reign of terror", "output": [["what emperor took over france after the reign of terror", "Emperor of the French\n\nNapoleon's reign lasted until 22 June 1815, when he was defeated at the Battle of Waterloo, exiled, and imprisoned on the island of Saint Helena, where he died on 5 May 1821. His reign was interrupted by the Bourbon Restoration of 1814 and his exile to Elba, from where he escaped less than a year later to reclaim the throne, reigning as Emperor for another 111 days before his final defeat and exile.\nLess than a year after the 1851 French coup d'état by Napoleon's nephew Louis-Napoléon Bonaparte, which ended in the successful dissolution of the French National Assembly, the French Second Republic was transformed into the Second French Empire, established by a referendum on 7 November 1852. President Bonaparte, elected by the French people, officially became Napoleon III, Emperor of the French, from the symbolic and historic date of 2 December 1852. His rule would de facto end on 28 July 1870 - the power of the head of state was transferred to his wife Eugenie de Montijo who would rule as empress regent of France while Napoleon III left with his army. His reign would nominally continue until 4 September 1870, as he was officially deposed after his defeat and capture at the Battle of Sedan during the Franco-Prussian War. In March 1871, he would be released from Prussian custody and exiled to the United Kingdom, where he died on 9 January 1873."]], "corpus": "wikipedia"}
{"tstamp": 1722305078.6563, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722305078.4572, "finish": 1722305078.6563, "ip": "", "conv_id": "053ee0d3184146fda38e9bbb641a0f03", "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": 1722305178.7998, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722305178.5602, "finish": 1722305178.7998, "ip": "", "conv_id": "a721ba1ec728453388b30d0efed8f994", "model_name": "GritLM/GritLM-7B", "prompt": "what is the difference between an electronic signal and an electromagnetic signal?", "output": [["what is the difference between an electronic signal and an electromagnetic signal?", "Gaming Stackexchange\n\nQ: What's the difference between semaphore and electric signals? In OpenTTD you can use electric signals and semaphore signals. In every instance I've ever seen people use electric signals to direct their trains. On the wiki the only mention of semaphores is \"don't hold down control otherwise you'll end up with a semaphore\". This suggests to me that semaphores are unwanted and have a different function to normal signals that is only applicable in complex circumstances. I couldn't find anything on the internet detailing their function. \nWhat is the functional difference between semaphore and electric signals?\n\nA: There is no functional difference between the two. By default the game will construct semaphores before a certain date defined in the Advanced Game Options > Construction section, to mimic the introduction of electrical signals in real life. Holding down Ctrl overrides that and builds semaphores instead. \n"]], "corpus": "stackexchange"}
{"tstamp": 1722305178.7998, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1722305178.5602, "finish": 1722305178.7998, "ip": "", "conv_id": "03037905b8a749e399e111e32c19baee", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "what is the difference between an electronic signal and an electromagnetic signal?", "output": [["what is the difference between an electronic signal and an electromagnetic signal?", "Askubuntu Stackexchange\n\nQ: Trouble Installing libxp6 and multi-arch-support I am following this guide, but I have run into some difficulties at step 11. When I type\nsudo apt-get install ./libxp6_1.0.2-2_amd64.deb\n\nI get\nReading package lists... Done\nBuilding dependency tree       \nReading state information... Done\nNote, selecting 'libxp6' instead of './libxp6_1.0.2-2_amd64.deb'\nSome packages could not be installed. This may mean that you have\nrequested an impossible situation or if you are using the unstable\ndistribution that some required packages have not yet been created\nor been moved out of Incoming.\nThe following information may help to resolve the situation:\n\nThe following packages have unmet dependencies:\n libxp6 : PreDepends: multiarch-support but it is not installable\nE: Unable to correct problems, you have held broken packages.\n\nlibxp6 and multiarch-support are what seem to be giving me difficulties. Searching on the web has given me a little, but not enough to install. Even the Ubuntu package website isn't helping.\nAny help would be appreciated.\n\nA: Then you have to install multiarch-support package from 18.04 LTS first with\ncd ~/Downloads\nwget http://archive.ubuntu.com/ubuntu/pool/main/g/glibc/multiarch-support_2.27-3ubuntu1_amd64.deb\nsudo apt-get install ./multiarch-support_2.27-3ubuntu1_amd64.deb\n\nand continue to follow the guide.\n"]], "corpus": "stackexchange"}
{"tstamp": 1722305223.7026, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722305223.4442, "finish": 1722305223.7026, "ip": "", "conv_id": "9f4dfa402ea64c74bfec3768569b6d00", "model_name": "GritLM/GritLM-7B", "prompt": "How can deep learning be used to design communication systems as end-to-end reconstruction tasks?  ", "output": [["How can deep learning be used to design communication systems as end-to-end reconstruction tasks?  ", "Title: An Introduction to Deep Learning for the Physical Layer\n\nAbstract: We present and discuss several novel applications of deep learning for the physical layer. By interpreting a communications system as an autoencoder, we develop a fundamental new way to think about communications system design as an end-to-end reconstruction task that seeks to jointly optimize transmitter and receiver components in a single process. We show how this idea can be extended to networks of multiple transmitters and receivers and present the concept of radio transformer networks as a means to incorporate expert domain knowledge in the machine learning model. Lastly, we demonstrate the application of convolutional neural networks on raw IQ samples for modulation classification which achieves competitive accuracy with respect to traditional schemes relying on expert features. The paper is concluded with a discussion of open challenges and areas for future investigation."]], "corpus": "arxiv"}
{"tstamp": 1722305223.7026, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722305223.4442, "finish": 1722305223.7026, "ip": "", "conv_id": "fb3390a4534f41758663e225ee7ed9a6", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "How can deep learning be used to design communication systems as end-to-end reconstruction tasks?  ", "output": [["How can deep learning be used to design communication systems as end-to-end reconstruction tasks?  ", "Title: Design of Communication Systems using Deep Learning: A Variational Inference Perspective\n\nAbstract: Recent research in the design of end to end communication system using deep learning has produced models which can outperform traditional communication schemes. Most of these architectures leveraged autoencoders to design the encoder at the transmitter and decoder at the receiver and train them jointly by modeling transmit symbols as latent codes from the encoder. However, in communication systems, the receiver has to work with noise corrupted versions of transmit symbols. Traditional autoencoders are not designed to work with latent codes corrupted with noise. In this work, we provide a framework to design end to end communication systems which accounts for the existence of noise corrupted transmit symbols. The proposed method uses deep neural architecture. An objective function for optimizing these models is derived based on the concepts of variational inference. Further, domain knowledge such as channel type can be systematically integrated into the objective. Through numerical simulation, the proposed method is shown to consistently produce models with better packing density and achieving it faster in multiple popular channel models as compared to the previous works leveraging deep learning models."]], "corpus": "arxiv"}
{"tstamp": 1722305229.7963, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722305229.6152, "finish": 1722305229.7963, "ip": "", "conv_id": "6cf9d4db20f8436c8c7f2ccaca29d2fa", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "what is the difference between cluster point and limit point?", "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"]], "corpus": "stackexchange"}
{"tstamp": 1722305229.7963, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722305229.6152, "finish": 1722305229.7963, "ip": "", "conv_id": "38308d8cc72f4a24b8ddc6bafec79a96", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "what is the difference between cluster point and limit point?", "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"]], "corpus": "stackexchange"}
{"tstamp": 1722305252.6945, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722305252.201, "finish": 1722305252.6945, "ip": "", "conv_id": "d56443b4b3064e69a3fc99eabc8fbf8f", "model_name": "BM25", "prompt": "mass effect 1 how many skill points total?", "output": [["mass effect 1 how many skill points total?", "Gaming Stackexchange\n\nQ: How many skill points will I have at max level? What is the maximum level in Mass Effect 3?\nHow many skill points will Shepard have at max level? \nIf NPC companions earn a different number of skill points than Shepard, what's the number they'll have at max level?\n\nA: Since this question seems to have been deemed the Canonical by mods:\nThe maximum character level is 60. At this level, Shepard will have earned a total of 181 Skill Points.\nMaxing a single skill requires 21 points.\nThere are enough points in the game to max out a total of 8 skills, with a few left over.\nShepard, conveniently enough, has 8 skills, plus one slot for a Bonus Power.\nSo, you can come very close to maxing out all skills, but you can't quite make it to maxing out all 9. You can max out 8 of them, and have 13 points remaining for the 9th, which lets you get to rank 4. You'll have 2 points left over. Alternately, you could max out 7 skills, get two to rank 5, and have 3 points left over.\n"]], "corpus": "stackexchange"}
{"tstamp": 1722305252.6945, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722305252.201, "finish": 1722305252.6945, "ip": "", "conv_id": "2766c8817d564f3fa47a1aa8eeaa0b03", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "mass effect 1 how many skill points total?", "output": [["mass effect 1 how many skill points total?", "Gaming Stackexchange\n\nQ: How many skill points will I have at max level? What is the maximum level in Mass Effect 3?\nHow many skill points will Shepard have at max level? \nIf NPC companions earn a different number of skill points than Shepard, what's the number they'll have at max level?\n\nA: Since this question seems to have been deemed the Canonical by mods:\nThe maximum character level is 60. At this level, Shepard will have earned a total of 181 Skill Points.\nMaxing a single skill requires 21 points.\nThere are enough points in the game to max out a total of 8 skills, with a few left over.\nShepard, conveniently enough, has 8 skills, plus one slot for a Bonus Power.\nSo, you can come very close to maxing out all skills, but you can't quite make it to maxing out all 9. You can max out 8 of them, and have 13 points remaining for the 9th, which lets you get to rank 4. You'll have 2 points left over. Alternately, you could max out 7 skills, get two to rank 5, and have 3 points left over.\n"]], "corpus": "stackexchange"}
{"tstamp": 1722305639.8104, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722305639.6393, "finish": 1722305639.8104, "ip": "", "conv_id": "bc1a7d6500334478abf4839584bbeaec", "model_name": "GritLM/GritLM-7B", "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: Brain Network Transformer\n\nAbstract: Human brains are commonly modeled as networks of Regions of Interest (ROIs) and their connections for the understanding of brain functions and mental disorders. Recently, Transformer-based models have been studied over different types of data, including graphs, shown to bring performance gains widely. In this work, we study Transformer-based models for brain network analysis. Driven by the unique properties of data, we model brain networks as graphs with nodes of fixed size and order, which allows us to (1) use connection profiles as node features to provide natural and low-cost positional information and (2) learn pair-wise connection strengths among ROIs with efficient attention weights across individuals that are predictive towards downstream analysis tasks. Moreover, we propose an Orthonormal Clustering Readout operation based on self-supervised soft clustering and orthonormal projection. This design accounts for the underlying functional modules that determine similar behaviors among groups of ROIs, leading to distinguishable cluster-aware node embeddings and informative graph embeddings. Finally, we re-standardize the evaluation pipeline on the only one publicly available large-scale brain network dataset of ABIDE, to enable meaningful comparison of different models. Experiment results show clear improvements of our proposed Brain Network Transformer on both the public ABIDE and our restricted ABCD datasets. The implementation is available at https://github.com/Wayfear/BrainNetworkTransformer."]], "corpus": "arxiv"}
{"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 2–0 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 5–1 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 7–3 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 1949–50 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 1–0 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"}
{"tstamp": 1722306925.7354, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722306925.3084, "finish": 1722306925.7354, "ip": "", "conv_id": "f027bc84f35a4d748c67b44a2435bbe4", "model_name": "voyage-multilingual-2", "prompt": "What is a transformer?", "output": [["What is a transformer?", "Title: Formal Algorithms for Transformers\n\nAbstract: This document aims to be a self-contained, mathematically precise overview of transformer architectures and algorithms (*not* results). It covers what transformers are, how they are trained, what they are used for, their key architectural components, and a preview of the most prominent models. The reader is assumed to be familiar with basic ML terminology and simpler neural network architectures such as MLPs."]], "corpus": "arxiv"}
{"tstamp": 1722306971.5819, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722306971.4007, "finish": 1722306971.5819, "ip": "", "conv_id": "a55ff04a15fd4107ac10365fbb0eb9a2", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "where did the british empire control an entire continent", "output": [["where did the british empire control an entire continent", "British Empire\n\nThe British Empire comprised the dominions, colonies, protectorates, mandates, and other territories ruled or administered by the United Kingdom and its predecessor states. It began with the overseas possessions and trading posts established by England in the late 16th and early 17th centuries. At its height in the 19th and early 20th centuries, it was the largest empire in history and, for a century, was the foremost global power. By 1913, the British Empire held sway over 412 million people,  of the world population at the time, and by 1920, it covered ,  of the Earth's total land area. As a result, its constitutional, legal, linguistic, and cultural legacy is widespread. At the peak of its power, it was described as \"the empire on which the sun never sets\", as the sun was always shining on at least one of its territories.\nDuring the Age of Discovery in the 15th and 16th centuries, Portugal and Spain pioneered European exploration of the globe, and in the process established large overseas empires. Envious of the great wealth these empires generated, England, France, and the Netherlands began to establish colonies and trade networks of their own in the Americas and Asia. A series of wars in the 17th and 18th centuries with the Netherlands and France left England (Britain, following the 1707 Act of Union with Scotland) the dominant colonial power in North America. Britain became a major power in the Indian subcontinent after the East India Company's conquest of Mughal Bengal at the Battle of Plassey in 1757."]], "corpus": "wikipedia"}
{"tstamp": 1722306971.5819, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722306971.4007, "finish": 1722306971.5819, "ip": "", "conv_id": "a6afd35e6fa54368be503cb1c3cf43ea", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "where did the british empire control an entire continent", "output": [["where did the british empire control an entire continent", "Territorial evolution of the British Empire\n\nThe territorial evolution of the British Empire is considered to have begun with the foundation of the English colonial empire in the late 16th century.  Since then, many territories around the world have been under the control of the United Kingdom or its predecessor states.\nWhen the Kingdom of Great Britain was formed in 1707 by the union of the Kingdoms of Scotland and England, the latter country's colonial possessions passed to the new state. Similarly, when Great Britain was united with the Kingdom of Ireland in 1801 to form the United Kingdom, control over its colonial possessions passed to the latter state. Collectively, these territories are referred to as the British Empire. When much of Ireland gained independence in 1922 as the Irish Free State, the other territories of the empire remained under the control of the United Kingdom.\nFrom 1714 to 1837, the British throne was held by a series of kings who were also the rulers of the German state of Hanover.  However, this was purely a personal union, with Hanover maintaining its political independence otherwise, and so it is not usually considered to have formed part of the British Empire."]], "corpus": "wikipedia"}
{"tstamp": 1722306986.0121, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722306985.8309, "finish": 1722306986.0121, "ip": "", "conv_id": "734d33bae026481cab60cb1e123a36b4", "model_name": "GritLM/GritLM-7B", "prompt": "Is attention all I need?", "output": [["Is attention all I need?", "Title: Attention Is All You Need\n\nAbstract: The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data."]], "corpus": "arxiv"}
{"tstamp": 1722307003.9939, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722307003.6084, "finish": 1722307003.9939, "ip": "", "conv_id": "3c77700a297d4a26b9466b34301c929b", "model_name": "voyage-multilingual-2", "prompt": "Is attention all I need?", "output": [["Is attention all I need?", "Title: Visual Attention and its Intimate Links to Spatial Cognition\n\nAbstract: It is almost universal to regard attention as the facility that permits an agent, human or machine, to give priority processing resources to relevant stimuli while ignoring the irrelevant. The reality of how this might manifest itself throughout all the forms of perceptual and cognitive processes possessed by humans, however, is not as clear. Here we examine this reality with a broad perspective in order to highlight the myriad ways that attentional processes impact both perception and cognition. The paper concludes by showing two real world problems that exhibit sufficient complexity to illustrate the ways in which attention and cognition connect. These then point to new avenues of research that might illuminate the overall cognitive architecture of spatial cognition."]], "corpus": "arxiv"}
{"tstamp": 1722307006.8942, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722307006.267, "finish": 1722307006.8942, "ip": "", "conv_id": "fdbaddd493b84cc89357c4480e2d727d", "model_name": "voyage-multilingual-2", "prompt": "The effect of Long pimple ", "output": [["The effect of Long pimple ", "Pimple\n\nA pimple or zit is a kind of comedo that results from excess sebum and dead skin cells getting trapped in the pores of the skin. In its aggravated state, it may evolve into a pustule or papule. Pimples can be treated by acne medications, antibiotics, and anti-inflammatories prescribed by a physician, or various over the counter remedies purchased at a pharmacy.\nCauses\nSebaceous glands inside the pore of the skin produce sebum. When the outer layers of skin shed (a natural and continuous process, normally), dead skin and oily sebum left behind may bond together and form a blockage of the sebaceous gland at the base of the skin. This is most common when the skin becomes thicker at puberty. The sebaceous gland continues to produce sebum, which builds up behind the blockage, allowing bacteria to grow in the area, including the species Staphylococcus aureus and Cutibacterium acnes, which causes inflammation and infection. Other causes of pimples include family history, stress, fluctuations in hormone levels, hair and skincare products, medication side effects, and un-diagnosed or underlying medical conditions. Pimples can be part of the presentation of rosacea."]], "corpus": "wikipedia"}
{"tstamp": 1722307006.8942, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722307006.267, "finish": 1722307006.8942, "ip": "", "conv_id": "86047e9633f3430da6d888564b950f65", "model_name": "text-embedding-3-large", "prompt": "The effect of Long pimple ", "output": [["The effect of Long pimple ", "Table tennis grips and playing styles\n\nShort pips hitter This penhold style utilizes a short pips out rubber, usually on only one side of the racket with no backside rubber. Short pips hitters play over the table, hitting the ball very flatly as soon as it bounces off the table with the pips being largely unaffected by (or even negating) the majority of the opponent's spin. The advantage of hitting the ball flatly is that all of the player's power is transferred into the linear velocity of the ball, without expending any energy on creating spin. This means that the shots are very fast, and typically hard to block for the inexperienced player. However, the lack of a topspin Magnus effect means that the shots are harder to place on the table since they don't follow a downward arc trajectory, and the lack of angular momentum on the ball means that shots are less consistent and more sensitive to small amounts of wind or air pressure, often giving a knuckle ball effect.\nNotable penhold short pips hitters include 1996 Olympic Champion Liu Guoliang, 1985 and 1987 World Champion Jiang Jialiang, three-time Olympian Toshio Tasaki, Yang Ying, Kwak Bang-bang, Seok Eun-mi, He Zhi Wen, Lee Eun-hee, Wang Zeng Yi and Rory Cargill.\nShakehand playing styles"]], "corpus": "wikipedia"}
{"tstamp": 1722307064.6599, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722307064.0314, "finish": 1722307064.6599, "ip": "", "conv_id": "a10afac3a30a4e82b4163e080b19e9e9", "model_name": "text-embedding-3-large", "prompt": "Is attention all I need?", "output": [["Is attention all I need?", "Title: Is Attention All What You Need? -- An Empirical Investigation on Convolution-Based Active Memory and Self-Attention\n\nAbstract: The key to a Transformer model is the self-attention mechanism, which allows the model to analyze an entire sequence in a computationally efficient manner. Recent work has suggested the possibility that general attention mechanisms used by RNNs could be replaced by active-memory mechanisms. In this work, we evaluate whether various active-memory mechanisms could replace self-attention in a Transformer. Our experiments suggest that active-memory alone achieves comparable results to the self-attention mechanism for language modelling, but optimal results are mostly achieved by using both active-memory and self-attention mechanisms together. We also note that, for some specific algorithmic tasks, active-memory mechanisms alone outperform both self-attention and a combination of the two."]], "corpus": "arxiv"}
{"tstamp": 1722307104.0574, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722307103.5502, "finish": 1722307104.0574, "ip": "", "conv_id": "3fe38b7d37f6440f9394145fd67cb9a9", "model_name": "text-embedding-3-large", "prompt": "The effect of long Pimple ", "output": [["The effect of long Pimple ", "Table tennis grips and playing styles\n\nShort pips hitter This penhold style utilizes a short pips out rubber, usually on only one side of the racket with no backside rubber. Short pips hitters play over the table, hitting the ball very flatly as soon as it bounces off the table with the pips being largely unaffected by (or even negating) the majority of the opponent's spin. The advantage of hitting the ball flatly is that all of the player's power is transferred into the linear velocity of the ball, without expending any energy on creating spin. This means that the shots are very fast, and typically hard to block for the inexperienced player. However, the lack of a topspin Magnus effect means that the shots are harder to place on the table since they don't follow a downward arc trajectory, and the lack of angular momentum on the ball means that shots are less consistent and more sensitive to small amounts of wind or air pressure, often giving a knuckle ball effect.\nNotable penhold short pips hitters include 1996 Olympic Champion Liu Guoliang, 1985 and 1987 World Champion Jiang Jialiang, three-time Olympian Toshio Tasaki, Yang Ying, Kwak Bang-bang, Seok Eun-mi, He Zhi Wen, Lee Eun-hee, Wang Zeng Yi and Rory Cargill.\nShakehand playing styles"]], "corpus": "wikipedia"}
{"tstamp": 1722307112.9208, "task_type": "retrieval", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722307112.5638, "finish": 1722307112.9208, "ip": "", "conv_id": "3754c3b0f9fd448eac01d2cebd94e498", "model_name": "embed-english-v3.0", "prompt": "Is attention all I need?", "output": [["Is attention all I need?", "Title: Attention is all they need: Cognitive science and the (techno)political economy of attention in humans and machines\n\nAbstract: This paper critically analyses the \"attention economy\" within the framework of cognitive science and techno-political economics, as applied to both human and machine interactions. We explore how current business models, particularly in digital platform capitalism, harness user engagement by strategically shaping attentional patterns. These platforms utilize advanced AI and massive data analytics to enhance user engagement, creating a cycle of attention capture and data extraction. We review contemporary (neuro)cognitive theories of attention and platform engagement design techniques and criticize classical cognitivist and behaviourist theories for their inadequacies in addressing the potential harms of such engagement on user autonomy and wellbeing. 4E approaches to cognitive science, instead, emphasizing the embodied, extended, enactive, and ecological aspects of cognition, offer us an intrinsic normative standpoint and a more integrated understanding of how attentional patterns are actively constituted by adaptive digital environments. By examining the precarious nature of habit formation in digital contexts, we reveal the techno-economic underpinnings that threaten personal autonomy by disaggregating habits away from the individual, into an AI managed collection of behavioural patterns. Our current predicament suggests the necessity of a paradigm shift towards an ecology of attention. This shift aims to foster environments that respect and preserve human cognitive and social capacities, countering the exploitative tendencies of cognitive capitalism."]], "corpus": "arxiv"}
{"tstamp": 1722307122.7157, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722307120.4275, "finish": 1722307122.7157, "ip": "", "conv_id": "ea468cdfbe3a4dd680a9a7471bed7154", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "The effect of long pimple", "output": [["The effect of long pimple", "Table tennis grips and playing styles\n\nShort pips hitter This penhold style utilizes a short pips out rubber, usually on only one side of the racket with no backside rubber. Short pips hitters play over the table, hitting the ball very flatly as soon as it bounces off the table with the pips being largely unaffected by (or even negating) the majority of the opponent's spin. The advantage of hitting the ball flatly is that all of the player's power is transferred into the linear velocity of the ball, without expending any energy on creating spin. This means that the shots are very fast, and typically hard to block for the inexperienced player. However, the lack of a topspin Magnus effect means that the shots are harder to place on the table since they don't follow a downward arc trajectory, and the lack of angular momentum on the ball means that shots are less consistent and more sensitive to small amounts of wind or air pressure, often giving a knuckle ball effect.\nNotable penhold short pips hitters include 1996 Olympic Champion Liu Guoliang, 1985 and 1987 World Champion Jiang Jialiang, three-time Olympian Toshio Tasaki, Yang Ying, Kwak Bang-bang, Seok Eun-mi, He Zhi Wen, Lee Eun-hee, Wang Zeng Yi and Rory Cargill.\nShakehand playing styles"]], "corpus": "wikipedia"}
{"tstamp": 1722307143.5212, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722307143.3926, "finish": 1722307143.5212, "ip": "", "conv_id": "ef28c889daf44e7587446e62092e3d2a", "model_name": "GritLM/GritLM-7B", "prompt": "Is attention all I need?", "output": [["Is attention all I need?", "Title: Attention Is All You Need\n\nAbstract: The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data."]], "corpus": "arxiv"}
{"tstamp": 1722307566.3844, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722307566.1887, "finish": 1722307566.3844, "ip": "", "conv_id": "9deb3465ffd3458081570489c3810813", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "are opened or are open?", "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"]], "corpus": "stackexchange"}
{"tstamp": 1722307566.3844, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722307566.1887, "finish": 1722307566.3844, "ip": "", "conv_id": "9913e8b8325c4550a0bfb3f5298189d2", "model_name": "BM25", "prompt": "are opened or are open?", "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"]], "corpus": "stackexchange"}
{"tstamp": 1722309436.1545, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722309435.9519, "finish": 1722309436.1545, "ip": "", "conv_id": "38a0e89319e9427ea9e2b12967ebecca", "model_name": "BM25", "prompt": "are brake pads supposed to be loose?", "output": [["are brake pads supposed to be loose?", "Stackoverflow Stackexchange\n\nQ: In Scala, how do I mixin java interfaces into Scala code For example suppose I have\ninterface ICar {...}\nclass Car implements ICar {...}\n\nIn Scala I wish to do\nnew MyScalaClass with ICar\n\nBut use the java implementation of ICar i.e. Car. What is the syntax for doing this?\n\nA: You can use object aggregation, but encapsulating the aggregation in a trait. Suppose you have the following Java code:\ninterface ICar {\n  public void brake();\n}\npublic class Car implements ICar {\n  public void brake() { System.out.println(\"BRAKE !!!\"); }\n}\n\nThen you can define the following Scala trait:\ntrait HasCar { self: ICar =>\n  private val car = new Car\n  def brake() = car.brake()\n}\n\nAnd finally you can mix everything you need into your class:\n val c = new MyScalaClass extends ICar with HasCar\n c.brake // prints \"BRAKE !!!\"\n\n"]], "corpus": "stackexchange"}
{"tstamp": 1722309436.1545, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722309435.9519, "finish": 1722309436.1545, "ip": "", "conv_id": "9af9eff85d5f4a178cd5808f666b4e57", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "are brake pads supposed to be loose?", "output": [["are brake pads supposed to be loose?", "Gaming Stackexchange\n\nQ: Brake pedal trouble with Xbox 360 Racing Wheel I have bought a used Microsoft Xbox Racing Wheel and have recently started playing. I have noticed that every time I let go of the gas pedal completely, the braking lights turn on. And when the car eventually stops, it starts backing up in reverse at full speed.\nHas anyone else experienced this? It happens on any game. I also switched pedal sets as I have 2 sets. \n\nA: I had this problem with mine, and it turned out that I needed to change out my batteries.  There is a compartment on the steering wheel portion of the setup that opens up when you push a grey button, and that's where the batteries go.  I want to say that it's all the way over on the right side of the assembly, but I am not 100% sure, as I do not have it in front of me.  It's pretty easy to find, once you know it's up there, though. If you can, use the kind of batteries meant for digital cameras, etc; the wheel draws a bit more juice than a normal XB360 controller.\n"]], "corpus": "stackexchange"}
{"tstamp": 1722309446.5042, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722309446.2777, "finish": 1722309446.5042, "ip": "", "conv_id": "bc8a08f1718846e58aa907a16ce9620b", "model_name": "GritLM/GritLM-7B", "prompt": "where did german immigrants settled in the 1800s", "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."]], "corpus": "wikipedia"}
{"tstamp": 1722309446.5042, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722309446.2777, "finish": 1722309446.5042, "ip": "", "conv_id": "b7cfab5df92f40cb97e749a1057688d3", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "where did german immigrants settled in the 1800s", "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."]], "corpus": "wikipedia"}
{"tstamp": 1722310041.3916, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722310041.2042, "finish": 1722310041.3916, "ip": "", "conv_id": "0b27bd2d434548c2960eabd134fec901", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "Lightweight and efficient self-attentive model improving performance of sequential recommenders.", "output": [["Lightweight and efficient self-attentive model improving performance of sequential recommenders.", "Title: Infinite Lexicographic Products of Triangular Algebras\n\nAbstract: Some new connections are given between linear orderings and triangular operator algebras. A lexicograhic product is defined for triangular operator algebras and the Jacobson radical of an infinite lexicographic product of upper triangular matrix algebras is determined."]], "corpus": "arxiv"}
{"tstamp": 1722310041.3916, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722310041.2042, "finish": 1722310041.3916, "ip": "", "conv_id": "29c8e18ebfb3415483d9b644aadd230c", "model_name": "GritLM/GritLM-7B", "prompt": "Lightweight and efficient self-attentive model improving performance of sequential recommenders.", "output": [["Lightweight and efficient self-attentive model improving performance of sequential recommenders.", "Title: Lightweight Self-Attentive Sequential Recommendation\n\nAbstract: Modern deep neural networks (DNNs) have greatly facilitated the development of sequential recommender systems by achieving state-of-the-art recommendation performance on various sequential recommendation tasks. Given a sequence of interacted items, existing DNN-based sequential recommenders commonly embed each item into a unique vector to support subsequent computations of the user interest. However, due to the potentially large number of items, the over-parameterised item embedding matrix of a sequential recommender has become a memory bottleneck for efficient deployment in resource-constrained environments, e.g., smartphones and other edge devices. Furthermore, we observe that the widely-used multi-head self-attention, though being effective in modelling sequential dependencies among items, heavily relies on redundant attention units to fully capture both global and local item-item transition patterns within a sequence. In this paper, we introduce a novel lightweight self-attentive network (LSAN) for sequential recommendation. To aggressively compress the original embedding matrix, LSAN leverages the notion of compositional embeddings, where each item embedding is composed by merging a group of selected base embedding vectors derived from substantially smaller embedding matrices. Meanwhile, to account for the intrinsic dynamics of each item, we further propose a temporal context-aware embedding composition scheme. Besides, we develop an innovative twin-attention network that alleviates the redundancy of the traditional multi-head self-attention while retaining full capacity for capturing long- and short-term (i.e., global and local) item dependencies. Comprehensive experiments demonstrate that LSAN significantly advances the accuracy and memory efficiency of existing sequential recommenders."]], "corpus": "arxiv"}
{"tstamp": 1722310072.3579, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722310072.2064, "finish": 1722310072.3579, "ip": "", "conv_id": "723e06bd28704a6b90a00a505ecb7438", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "A new method called ATTEMPT utilizing attentional mixtures of soft prompts for knowledge transfer.", "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."]], "corpus": "arxiv"}
{"tstamp": 1722310072.3579, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722310072.2064, "finish": 1722310072.3579, "ip": "", "conv_id": "6584a2b86688459ca86f0cbfc241df00", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "A new method called ATTEMPT utilizing attentional mixtures of soft prompts for knowledge transfer.", "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."]], "corpus": "arxiv"}
{"tstamp": 1722310097.423, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722310094.8456, "finish": 1722310097.423, "ip": "", "conv_id": "daca567e0fff4693a219f1ccdcb2330c", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "who votes for nba defensive player of year", "output": [["who votes for nba defensive player of year", "NBA Defensive Player of the Year Award\n\nThe NBA Defensive Player of the Year Award is an annual National Basketball Association (NBA) award given since the 1982–83 NBA season to the best defensive player of the regular season. The winner is selected by a panel of 124 sportswriters and broadcasters throughout the United States and Canada, each of whom casts a vote for first, second and third place selections. Each first-place vote is worth five points, second-place votes are worth three points, and a third-place vote is worth one. The player with the highest point total, regardless of the number of first-place votes, wins the award. Since the 2022–23 NBA season, winners receive the Hakeem Olajuwon Trophy, named after the two-time defensive player of the year winner.\nDikembe Mutombo, Ben Wallace, and Rudy Gobert have each won the award a record four times. Dwight Howard has won the award three times, and is the only player ever to have won it in three consecutive seasons. Sidney Moncrief, Mark Eaton, Dennis Rodman, Hakeem Olajuwon, Alonzo Mourning and Kawhi Leonard have each won it twice. The most recent award recipient is Rudy Gobert of the Minnesota Timberwolves."]], "corpus": "wikipedia"}
{"tstamp": 1722310097.423, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722310094.8456, "finish": 1722310097.423, "ip": "", "conv_id": "2eb4d1f035c0457d8b0f19fc28c2af8b", "model_name": "text-embedding-3-large", "prompt": "who votes for nba defensive player of year", "output": [["who votes for nba defensive player of year", "NBA Defensive Player of the Year Award\n\nThe NBA Defensive Player of the Year Award is an annual National Basketball Association (NBA) award given since the 1982–83 NBA season to the best defensive player of the regular season. The winner is selected by a panel of 124 sportswriters and broadcasters throughout the United States and Canada, each of whom casts a vote for first, second and third place selections. Each first-place vote is worth five points, second-place votes are worth three points, and a third-place vote is worth one. The player with the highest point total, regardless of the number of first-place votes, wins the award. Since the 2022–23 NBA season, winners receive the Hakeem Olajuwon Trophy, named after the two-time defensive player of the year winner.\nDikembe Mutombo, Ben Wallace, and Rudy Gobert have each won the award a record four times. Dwight Howard has won the award three times, and is the only player ever to have won it in three consecutive seasons. Sidney Moncrief, Mark Eaton, Dennis Rodman, Hakeem Olajuwon, Alonzo Mourning and Kawhi Leonard have each won it twice. The most recent award recipient is Rudy Gobert of the Minnesota Timberwolves."]], "corpus": "wikipedia"}
{"tstamp": 1722310117.8599, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722310117.7326, "finish": 1722310117.8599, "ip": "", "conv_id": "fc308a6564e84da2b576fda8f5b0f500", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "Study on improving computational efficiency and reducing parameters in recommender systems using prompt tuning and pruning techniques", "output": [["Study on improving computational efficiency and reducing parameters in recommender systems using prompt tuning and pruning techniques", "Title: Spatio-temporal dynamics and plastic flow of vortices in superconductors with periodic arrays of pinning sites\n\nAbstract: We present simulations of flux-gradient-driven superconducting rigid vortices interacting with square and triangular arrays of columnar pinning sites in an increasing external magnetic field. These simulations allow us to quantitatively relate spatio-temporal microscopic information of the vortex lattice with typically measured macroscopic quantities, such as the magnetization $M(H)$. The flux lattice does not become completely commensurate with the pinning sites throughout the sample at the magnetization matching peaks, but forms a commensurate lattice in a region close to the edge of the sample. Matching fields related to unstable vortex configurations do not produce peaks in $M(H)$. We observe a variety of evolving complex flux profiles, including flat terraces or plateaus separated by winding current-carrying strings and, near the peaks in $M(H)$, plateaus only in certain regions, which move through the sample as the field increases."]], "corpus": "arxiv"}
{"tstamp": 1722310117.8599, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1722310117.7326, "finish": 1722310117.8599, "ip": "", "conv_id": "659b5d7e65414017a44941dc4860d179", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "Study on improving computational efficiency and reducing parameters in recommender systems using prompt tuning and pruning techniques", "output": [["Study on improving computational efficiency and reducing parameters in recommender systems using prompt tuning and pruning techniques", "Title: Local Hypercomplex Analyticity\n\nAbstract: The notion of analyticity is studied in the context of hypercomplex numbers. A critical review of the problems arising from the conventional approach is given. We describe a local analyticity condition which yields the desired type of hypercomplex solutions. The result is the definition of a generalized complex analyticity to hypercomplex space. 02.10.Tq/Vr, 02.30.-f/Dk, 02.90.+p"]], "corpus": "arxiv"}
{"tstamp": 1722310173.0824, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722310172.872, "finish": 1722310173.0824, "ip": "", "conv_id": "356862c28de946ae8a434438c62a06e4", "model_name": "GritLM/GritLM-7B", "prompt": "diablo 2 is not working on windows 7?", "output": [["diablo 2 is not working on windows 7?", "Gaming Stackexchange\n\nQ: Playing Diablo 1 on Windows 7 I just found an old copy of Diablo (the first game) in a box and whacked it into my laptop I purchased just yesterday.\nAfter the quick install I hit play and found that the screen remained black (nothing was rendering). There was however sound.\nI downloaded the patch but still nothing.\nWith it being a new laptop, I figure I might be missing some required software like Direct X maybe? I'm not really sure.\nIf anyone has any suggestions or knows how to fix this issue, that would be really helpful.\n\nA: This may sound weird (and I have actually done this to play Diablo on my Win 7 laptop), but right click on your desktop and click on Screen Resolution. Leave that open and then run Diablo.\n"]], "corpus": "stackexchange"}
{"tstamp": 1722310173.0824, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722310172.872, "finish": 1722310173.0824, "ip": "", "conv_id": "375b8963e38a4bd191128977a52d9b97", "model_name": "BM25", "prompt": "diablo 2 is not working on windows 7?", "output": [["diablo 2 is not working on windows 7?", "Gaming Stackexchange\n\nQ: How do I configure Diablo 2 in Wine to -skiptobnet and -w? I am using Ubuntu Gnome and Diablo 2 works ok with Wine. How do I add shortcut options to the game? I want the -skiptobnet and -w for windowed mode.\nPath is ~/.wine/drive_c/Program Files (x86)/Diablo II/Game.exe.\n\nA: I was able to get options to work using the windows .lnk file that was created in the wine start menu directory during installation.\nYou can launch Diablo II through terminal or create a new launcher using this command replacing PATH_TO_WINE and PATH_TO_SHORTCUT:\nenv WINEPREFIX=\"PATH_TO_WINE\" wine C:\\\\windows\\\\command\\\\start.exe PATH_TO_SHORTCUT -skiptobnet -w\n\nFor example:\nenv WINEPREFIX=\"/home/nonterrorist/.wine\" wine C:\\\\windows\\\\command\\\\start.exe /Unix /home/nonterrorist/.wine/dosdevices/c:/users/Public/Start\\ Menu/Programs/Diablo\\ II/Diablo\\ II\\ -\\ Lord\\ of\\ Destruction.lnk -skiptobnet -w\n\nNote in the example above, you will need to escape any spaces in the shortcut path using \\\n"]], "corpus": "stackexchange"}
{"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"}
{"tstamp": 1722310364.3913, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722310363.9154, "finish": 1722310364.3913, "ip": "", "conv_id": "2dafecf528e44c34a578dd2bad99f101", "model_name": "GritLM/GritLM-7B", "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\nFar from the Madding Crowd is a 2015 British romantic drama film directed by Thomas Vinterberg and starring Carey Mulligan, Matthias Schoenaerts, Tom Sturridge, Michael Sheen, and Juno Temple. An adaptation by David Nicholls of the 1874 novel Far from the Madding Crowd by Thomas Hardy, it is the fourth film adaptation of the novel."]], "corpus": "wikipedia"}
{"tstamp": 1722310685.9008, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722310684.8371, "finish": 1722310685.9008, "ip": "", "conv_id": "0494368f367f4ce891d1645f49397dce", "model_name": "text-embedding-004", "prompt": "what happened to sokka after the last airbender", "output": [["what happened to sokka after the last airbender", "Sokka\n\nSokka is a fictional character in Nickelodeon's animated television series Avatar: The Last Airbender and its sequel series The Legend of Korra. The character, created by Michael Dante DiMartino and Bryan Konietzko, is voiced by Jack DeSena in the original series and by Chris Hardwick in the sequel series. He is a warrior of the Water Tribe and the son of Chief Hakoda and Kya. In the live-action film adaptation, he is portrayed by Jackson Rathbone, while in the  live-action television series adaptation, he is portrayed by Ian Ousley."]], "corpus": "wikipedia"}
{"tstamp": 1722310685.9008, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722310684.8371, "finish": 1722310685.9008, "ip": "", "conv_id": "2a889447fde54cc5b596b7983ab9b19e", "model_name": "GritLM/GritLM-7B", "prompt": "what happened to sokka after the last airbender", "output": [["what happened to sokka after the last airbender", "List of Avatar: The Last Airbender characters\n\nSokka Sokka () (voiced by Jack DeSena in the original series, Chris Hardwick in the sequel series, portrayed by Jackson Rathbone in the live-action film, Ian Ousley in the live-action series) is a 15-year-old warrior of the Southern Water Tribe, and Katara's older brother. With no bending power of his own, Sokka relies largely on a metallic boomerang, a blunt metal club, a machete, and later a black jian, or sword, created from the metals of a meteorite. Surprisingly in an inhabitant of a mystical world, Sokka is an engineer and something of a jack-of-all-trades, in which respect he is easily able to understand the Fire Nation's advanced technology, and perfects the design of the hot air balloon. In addition, he is both heterodox and resourceful in his endeavors, and a source of comic relief throughout the series. Sokka was in love with the Northern Water Tribe princess Yue at the end of Book One and later shifted his affections to the Kyoshi Warriors' leader Suki in Books Two and Three. In the sequel series, flashbacks reveal Sokka was the first representative of the Southern Water Tribe to sit on the Republic City Council, and possibly its first chairman. He died a few years after Aang, when the next Avatar, Korra, was still a child."]], "corpus": "wikipedia"}
{"tstamp": 1722310758.6297, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722310758.4281, "finish": 1722310758.6297, "ip": "", "conv_id": "2ff5ab5b630040549b9bacc23cc1ea95", "model_name": "GritLM/GritLM-7B", "prompt": "do fled pokemon come back?", "output": [["do fled pokemon come back?", "Gaming Stackexchange\n\nQ: Do I have to physically return a gym to retrieve a pokemon when the gym is defeated? I'm thinking about leaving my Vaporeon at the gym near where I work. I know that I might be lucky to keep it there one or two days, but darnit, I wanna do it. So if when the gym falls, do I need to physically return to it to get my pokemon back? \n\nA: No you do not! It returns to you automatically. As of Android version 0.35.0/iOS version 1.50, ousted defenders return to you with 0 HP, per the patch notes.\n"]], "corpus": "stackexchange"}
{"tstamp": 1722310758.6297, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722310758.4281, "finish": 1722310758.6297, "ip": "", "conv_id": "f684351f5e9a4cbd9e9e2e62282e624c", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "do fled pokemon come back?", "output": [["do fled pokemon come back?", "Stackoverflow Stackexchange\n\nQ: How to find out cl.exe's built-in macros Does anyone know how could I find out which are cl.exe's builtin/predefined macros?\nFor example for gcc the following command line will list all the compiler's builtin macros\ngcc -dM -E - </dev/null\n\nEDIT: I'm interested in a way similar to gcc's that is \"ask the actual compiler\".\nThanks\n\nA: /P preprocessor flag will emit the currently active macros based on the project build settings. I am not sure if it is exactly the equivalent of gcc command you have shown. The output is in .I file.\n"]], "corpus": "stackexchange"}
{"tstamp": 1722310810.0352, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722310809.838, "finish": 1722310810.0352, "ip": "", "conv_id": "51662d2391894809a37610cb3c419e8a", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "what is the difference between field and sigma field?", "output": [["what is the difference between field and sigma field?", "Math Stackexchange\n\nQ: $\\sigma$-algebra vs. $\\sigma$-field: is there any difference? The subject says it all: is there any difference between the two concepts of $\\sigma$-algebra and $\\sigma$-field? In probability theory, they seem to be used more or less interchangeably. If there is no difference, is there any historical reason why some people/schools use the term $\\sigma$-algebra, while others use the term $\\sigma-$field?\n\nA: If you want to be completely sure you can see Taylor´s Introduction to Measure and Integration where he states that both concepts refer to the same thing.\n"]], "corpus": "stackexchange"}
{"tstamp": 1722310810.0352, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722310809.838, "finish": 1722310810.0352, "ip": "", "conv_id": "c60e866a11304ec09c51a718d9b9bbb8", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "what is the difference between field and sigma field?", "output": [["what is the difference between field and sigma field?", "Math Stackexchange\n\nQ: $\\sigma$-algebra vs. $\\sigma$-field: is there any difference? The subject says it all: is there any difference between the two concepts of $\\sigma$-algebra and $\\sigma$-field? In probability theory, they seem to be used more or less interchangeably. If there is no difference, is there any historical reason why some people/schools use the term $\\sigma$-algebra, while others use the term $\\sigma-$field?\n\nA: If you want to be completely sure you can see Taylor´s Introduction to Measure and Integration where he states that both concepts refer to the same thing.\n"]], "corpus": "stackexchange"}
{"tstamp": 1722310826.3889, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722310825.6751, "finish": 1722310826.3889, "ip": "", "conv_id": "3495497b338a42f68c1ed1ff16f8d3ab", "model_name": "BM25", "prompt": "a great benchmark for text embeddings", "output": [["a great benchmark for text embeddings", "Title: Recent advances in text embedding: A Comprehensive Review of Top-Performing Methods on the MTEB Benchmark\n\nAbstract: Text embedding methods have become increasingly popular in both industrial and academic fields due to their critical role in a variety of natural language processing tasks. The significance of universal text embeddings has been further highlighted with the rise of Large Language Models (LLMs) applications such as Retrieval-Augmented Systems (RAGs). While previous models have attempted to be general-purpose, they often struggle to generalize across tasks and domains. However, recent advancements in training data quantity, quality and diversity; synthetic data generation from LLMs as well as using LLMs as backbones encourage great improvements in pursuing universal text embeddings. In this paper, we provide an overview of the recent advances in universal text embedding models with a focus on the top performing text embeddings on Massive Text Embedding Benchmark (MTEB). Through detailed comparison and analysis, we highlight the key contributions and limitations in this area, and propose potentially inspiring future research directions."]], "corpus": "arxiv"}
{"tstamp": 1722310826.3889, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722310825.6751, "finish": 1722310826.3889, "ip": "", "conv_id": "732203ab60e34d408f5ea6db08909f9a", "model_name": "text-embedding-004", "prompt": "a great benchmark for text embeddings", "output": [["a great benchmark for 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."]], "corpus": "arxiv"}
{"tstamp": 1722310849.7255, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1722310847.5377, "finish": 1722310849.7255, "ip": "", "conv_id": "4f330aeb5b2b442d8190b75b12ed1f29", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "New search algorithm inspired by AlphaZero for better theorem proving results", "output": [["New search algorithm inspired by AlphaZero for better theorem proving results", "Title: On the propagation of semiclassical Wigner functions\n\nAbstract: We establish the difference between the propagation of semiclassical Wigner functions and classical Liouville propagation. First we re-discuss the semiclassical limit for the propagator of Wigner functions, which on its own leads to their classical propagation. Then, via stationary phase evaluation of the full integral evolution equation, using the semiclassical expressions of Wigner functions, we provide the correct geometrical prescription for their semiclassical propagation. This is determined by the classical trajectories of the tips of the chords defined by the initial semiclassical Wigner function and centered on their arguments, in contrast to the Liouville propagation which is determined by the classical trajectories of the arguments themselves."]], "corpus": "arxiv"}
{"tstamp": 1722310849.7255, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722310847.5377, "finish": 1722310849.7255, "ip": "", "conv_id": "86fc44429afe4954a77719d25bf4ef90", "model_name": "GritLM/GritLM-7B", "prompt": "New search algorithm inspired by AlphaZero for better theorem proving results", "output": [["New search algorithm inspired by AlphaZero for better theorem proving results", "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."]], "corpus": "arxiv"}
{"tstamp": 1722310874.1706, "task_type": "retrieval", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722310873.894, "finish": 1722310874.1706, "ip": "", "conv_id": "cbdc637378324499be0368c4cf0b44de", "model_name": "embed-english-v3.0", "prompt": "when was the abacus invented in ancient china", "output": [["when was the abacus invented in ancient china", "Abacus\n\nEgypt Greek historian Herodotus mentioned the abacus in Ancient Egypt. He wrote that the Egyptians manipulated the pebbles from right to left, opposite in direction to the Greek left-to-right method. Archaeologists have found ancient disks of various sizes that are thought to have been used as counters. However, wall depictions of this instrument are yet to be discovered.\nPersia\nAt around 600 BC, Persians first began to use the abacus, during the Achaemenid Empire. Under the Parthian, Sassanian, and Iranian empires, scholars concentrated on exchanging knowledge and inventions with the countries around them – India, China, and the Roman Empire- which is how the abacus may have been exported to other countries.\nGreece\nThe earliest archaeological evidence for the use of the Greek abacus dates to the 5th century BC. Demosthenes (384 BC–322 BC) complained that the need to use pebbles for calculations was too difficult. A play by Alexis from the 4th century BC mentions an abacus and pebbles for accounting, and both Diogenes and Polybius use the abacus as a metaphor for human behavior, stating \"that men that sometimes stood for more and sometimes for less\" like the pebbles on an abacus. The Greek abacus was a table of wood or marble, pre-set with small counters in wood or metal for mathematical calculations. This Greek abacus was used in Achaemenid Persia, the Etruscan civilization, Ancient Rome, and the Western Christian world until the French Revolution."]], "corpus": "wikipedia"}
{"tstamp": 1722310874.1706, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722310873.894, "finish": 1722310874.1706, "ip": "", "conv_id": "f78b452ffa04499e97fec3be70a373c3", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "when was the abacus invented in ancient china", "output": [["when was the abacus invented in ancient china", "Abacus\n\nEach rod typically represents one digit of a multi-digit number laid out using a positional numeral system such as base ten (though some cultures used different numerical bases). Roman and East Asian abacuses use a system resembling bi-quinary coded decimal, with a top deck (containing one or two beads) representing fives and a bottom deck (containing four or five beads) representing ones. Natural numbers are normally used, but some allow simple fractional components (e.g. , , and  in Roman abacus), and a decimal point can be imagined for fixed-point arithmetic.\nAny particular abacus design supports multiple methods to perform calculations, including addition, subtraction, multiplication, division, and square and cube roots. The beads are first arranged to represent a number, then are manipulated to perform a mathematical operation with another number, and their final position can be read as the result (or can be used as the starting number for subsequent operations).\nIn the ancient world, abacuses were a practical calculating tool. Although calculators and computers are commonly used today instead of abacuses, abacuses remain in everyday use in some countries. The abacus has an advantage of not requiring a writing implement and paper (needed for algorism) or an electric power source. Merchants, traders, and clerks in some parts of Eastern Europe, Russia, China, and Africa use abacuses. The abacus remains in common use as a scoring system in non-electronic table games. Others may use an abacus due to visual impairment that prevents the use of a calculator. The abacus is still used to teach the fundamentals of mathematics to children in most countries."]], "corpus": "wikipedia"}
{"tstamp": 1722310885.7903, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722310883.6154, "finish": 1722310885.7903, "ip": "", "conv_id": "c998ef114db443b0a8ea43dcaa79997c", "model_name": "GritLM/GritLM-7B", "prompt": "can I put metal plate in a microwave", "output": [["can I put metal plate in a microwave", "Microwave oven\n\nMetal objects Contrary to popular assumptions, metal objects can be safely used in a microwave oven, but with some restrictions. Any metal or conductive object placed into the microwave oven acts as an antenna to some degree, resulting in an electric current. This causes the object to act as a heating element. This effect varies with the object's shape and composition, and is sometimes utilized for cooking.\nAny object containing pointed metal can create an electric arc (sparks) when microwaved. This includes cutlery, crumpled aluminium foil (though some foil used in microwave ovens is safe, see below), twist-ties containing metal wire, the metal wire carry-handles in oyster pails, or almost any metal formed into a poorly conductive foil or thin wire, or into a pointed shape. Forks are a good example: the tines of the fork respond to the electric field by producing high concentrations of electric charge at the tips. This has the effect of exceeding the dielectric breakdown of air, about 3 megavolts per meter (3×106 V/m). The air forms a conductive plasma, which is visible as a spark. The plasma and the tines may then form a conductive loop, which may be a more effective antenna, resulting in a longer lived spark. When dielectric breakdown occurs in air, some ozone and nitrogen oxides are formed, both of which are unhealthy in large quantities."]], "corpus": "wikipedia"}
{"tstamp": 1722310885.7903, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722310883.6154, "finish": 1722310885.7903, "ip": "", "conv_id": "088ae8addacd4fa3a40cb59053e8abe6", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "can I put metal plate in a microwave", "output": [["can I put metal plate in a microwave", "Microwave oven\n\nMetal objects Contrary to popular assumptions, metal objects can be safely used in a microwave oven, but with some restrictions. Any metal or conductive object placed into the microwave oven acts as an antenna to some degree, resulting in an electric current. This causes the object to act as a heating element. This effect varies with the object's shape and composition, and is sometimes utilized for cooking.\nAny object containing pointed metal can create an electric arc (sparks) when microwaved. This includes cutlery, crumpled aluminium foil (though some foil used in microwave ovens is safe, see below), twist-ties containing metal wire, the metal wire carry-handles in oyster pails, or almost any metal formed into a poorly conductive foil or thin wire, or into a pointed shape. Forks are a good example: the tines of the fork respond to the electric field by producing high concentrations of electric charge at the tips. This has the effect of exceeding the dielectric breakdown of air, about 3 megavolts per meter (3×106 V/m). The air forms a conductive plasma, which is visible as a spark. The plasma and the tines may then form a conductive loop, which may be a more effective antenna, resulting in a longer lived spark. When dielectric breakdown occurs in air, some ozone and nitrogen oxides are formed, both of which are unhealthy in large quantities."]], "corpus": "wikipedia"}
{"tstamp": 1722310958.2128, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722310957.4932, "finish": 1722310958.2128, "ip": "", "conv_id": "b6d7ab0bc1bb42cb8e93438eef835621", "model_name": "GritLM/GritLM-7B", "prompt": "where does the spinal cord become cauda equina", "output": [["where does the spinal cord become cauda equina", "Cauda equina\n\nDevelopment In humans, the spinal cord stops growing in infancy.  At birth the end of the spinal cord is about the level of the third lumbar vertebra, or L3. Because the bones of the vertebral column continue to grow, by about 12 months of age the end of the cord reaches its permanent position. Typically this is at the level of L1 or L2 (closer to the head), ranging due to normal anatomical variations anywhere from the twelfth thoracic vertebra (T12) to L3. Individual spinal nerve roots arise from the cord as they get closer to the head, but as the differential growth occurs, the top end of the nerve stays attached to the spinal cord while the lower end of the nerve exits the spinal column at its proper level. This results in a \"bundle\"-like structure of nerve fibers that extends caudally, within the spinal column, from the end of the spinal cord, gradually declining in number further down as individual pairs leave the spinal column.\nClinical significance\nNeedle insertion\nThe cauda equina exists within the lumbar cistern, a gap between the arachnoid membrane and the pia mater of the spinal cord, called the subarachnoid space. Cerebrospinal fluid also exists within this space. Because the spinal cord terminates at level L1/L2, lumbar puncture (or colloquially, \"spinal tap\") is performed from the lumbar cistern between two vertebrae at level L3/L4, or L4/L5, where there is no risk of accidental injury to the spinal cord, when a sample of CSF is needed for clinical purposes."]], "corpus": "wikipedia"}
{"tstamp": 1722310958.2128, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722310957.4932, "finish": 1722310958.2128, "ip": "", "conv_id": "8dd7868f665946e3bc2d5ff340ff7f95", "model_name": "text-embedding-004", "prompt": "where does the spinal cord become cauda equina", "output": [["where does the spinal cord become cauda equina", "Cauda equina\n\nDevelopment In humans, the spinal cord stops growing in infancy.  At birth the end of the spinal cord is about the level of the third lumbar vertebra, or L3. Because the bones of the vertebral column continue to grow, by about 12 months of age the end of the cord reaches its permanent position. Typically this is at the level of L1 or L2 (closer to the head), ranging due to normal anatomical variations anywhere from the twelfth thoracic vertebra (T12) to L3. Individual spinal nerve roots arise from the cord as they get closer to the head, but as the differential growth occurs, the top end of the nerve stays attached to the spinal cord while the lower end of the nerve exits the spinal column at its proper level. This results in a \"bundle\"-like structure of nerve fibers that extends caudally, within the spinal column, from the end of the spinal cord, gradually declining in number further down as individual pairs leave the spinal column.\nClinical significance\nNeedle insertion\nThe cauda equina exists within the lumbar cistern, a gap between the arachnoid membrane and the pia mater of the spinal cord, called the subarachnoid space. Cerebrospinal fluid also exists within this space. Because the spinal cord terminates at level L1/L2, lumbar puncture (or colloquially, \"spinal tap\") is performed from the lumbar cistern between two vertebrae at level L3/L4, or L4/L5, where there is no risk of accidental injury to the spinal cord, when a sample of CSF is needed for clinical purposes."]], "corpus": "wikipedia"}
{"tstamp": 1722310988.5027, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722310987.9544, "finish": 1722310988.5027, "ip": "", "conv_id": "14396d4a28ae4f839eb1d20af5252e72", "model_name": "text-embedding-3-large", "prompt": "Transformer-based model designed specifically for Arabic language understanding", "output": [["Transformer-based model designed specifically for Arabic language understanding", "Title: Interpreting Arabic Transformer Models\n\nAbstract: Arabic is a Semitic language which is widely spoken with many dialects. Given the success of pre-trained language models, many transformer models trained on Arabic and its dialects have surfaced. While these models have been compared with respect to downstream NLP tasks, no evaluation has been carried out to directly compare the internal representations. We probe how linguistic information is encoded in Arabic pretrained models, trained on different varieties of Arabic language. We perform a layer and neuron analysis on the models using three intrinsic tasks: two morphological tagging tasks based on MSA (modern standard Arabic) and dialectal POS-tagging and a dialectal identification task. Our analysis enlightens interesting findings such as: i) word morphology is learned at the lower and middle layers ii) dialectal identification necessitate more knowledge and hence preserved even in the final layers, iii) despite a large overlap in their vocabulary, the MSA-based models fail to capture the nuances of Arabic dialects, iv) we found that neurons in embedding layers are polysemous in nature, while the neurons in middle layers are exclusive to specific properties."]], "corpus": "arxiv"}
{"tstamp": 1722310988.5027, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722310987.9544, "finish": 1722310988.5027, "ip": "", "conv_id": "f9f953034cbb4a10a1b425e550d64406", "model_name": "voyage-multilingual-2", "prompt": "Transformer-based model designed specifically for Arabic language understanding", "output": [["Transformer-based model designed specifically for Arabic language understanding", "Title: AraBERT: Transformer-based Model for Arabic Language Understanding\n\nAbstract: The Arabic language is a morphologically rich language with relatively few resources and a less explored syntax compared to English. Given these limitations, Arabic Natural Language Processing (NLP) tasks like Sentiment Analysis (SA), Named Entity Recognition (NER), and Question Answering (QA), have proven to be very challenging to tackle. Recently, with the surge of transformers based models, language-specific BERT based models have proven to be very efficient at language understanding, provided they are pre-trained on a very large corpus. Such models were able to set new standards and achieve state-of-the-art results for most NLP tasks. In this paper, we pre-trained BERT specifically for the Arabic language in the pursuit of achieving the same success that BERT did for the English language. The performance of AraBERT is compared to multilingual BERT from Google and other state-of-the-art approaches. The results showed that the newly developed AraBERT achieved state-of-the-art performance on most tested Arabic NLP tasks. The pretrained araBERT models are publicly available on https://github.com/aub-mind/arabert hoping to encourage research and applications for Arabic NLP."]], "corpus": "arxiv"}
{"tstamp": 1722311017.1892, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722311016.9641, "finish": 1722311017.1892, "ip": "", "conv_id": "42b61a56dbda4f8fb93f621e039f5ce4", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "do switches use ip or mac?", "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"]], "corpus": "stackexchange"}
{"tstamp": 1722311017.1892, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722311016.9641, "finish": 1722311017.1892, "ip": "", "conv_id": "ae5bb24c1351491fa7a7bb739099f035", "model_name": "BM25", "prompt": "do switches use ip or mac?", "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"]], "corpus": "stackexchange"}
{"tstamp": 1722311040.472, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722311039.6792, "finish": 1722311040.472, "ip": "", "conv_id": "8472d85456e841798101c9a3e9d1d29a", "model_name": "voyage-multilingual-2", "prompt": "can I do multithreading with fastapi", "output": [["can I do multithreading with fastapi", "FastAPI\n\nFastAPI is a modern web framework first released in 2018 for building HTTP-based service APIs in Python. It is used for building APIs with Python 3.8+ based on standard Python-type hints.\nFastAPI is based on Pydantic and uses type hints to validate, serialize and deserialize data. It also automatically generates OpenAPI documentation for APIs built with it.\nComponents\nPydantic\nPydantic is a data validation library for Python. While writing code in an IDE, Pydantic provides type hints for schema validation and serialization through type annotations.\nStarlette\nStarlette is a lightweight ASGI framework/toolkit, to support async functionality in Python.\nUvicorn\nUvicorn is a minimal low-level server/application web server for async frameworks, following the ASGI specification. Technically, it implements a multi-process model with one main process, which is responsible for managing a pool of worker processes and distributing incoming HTTP requests to them. The number of worker processes is pre-configured, but can also be adjusted up or down at runtime.\nExample\nThe following code shows a simple web application that displays \"Hello World!\" when visited:\nfrom fastapi import FastAPI\napp = FastAPI()\n@app.get(\"/\")\ndef read_root():"]], "corpus": "wikipedia"}
{"tstamp": 1722311040.472, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722311039.6792, "finish": 1722311040.472, "ip": "", "conv_id": "b987e3526f4e4c9fbea6f0eae988d918", "model_name": "text-embedding-004", "prompt": "can I do multithreading with fastapi", "output": [["can I do multithreading with fastapi", "FastAPI\n\nFastAPI is a modern web framework first released in 2018 for building HTTP-based service APIs in Python. It is used for building APIs with Python 3.8+ based on standard Python-type hints.\nFastAPI is based on Pydantic and uses type hints to validate, serialize and deserialize data. It also automatically generates OpenAPI documentation for APIs built with it.\nComponents\nPydantic\nPydantic is a data validation library for Python. While writing code in an IDE, Pydantic provides type hints for schema validation and serialization through type annotations.\nStarlette\nStarlette is a lightweight ASGI framework/toolkit, to support async functionality in Python.\nUvicorn\nUvicorn is a minimal low-level server/application web server for async frameworks, following the ASGI specification. Technically, it implements a multi-process model with one main process, which is responsible for managing a pool of worker processes and distributing incoming HTTP requests to them. The number of worker processes is pre-configured, but can also be adjusted up or down at runtime.\nExample\nThe following code shows a simple web application that displays \"Hello World!\" when visited:\nfrom fastapi import FastAPI\napp = FastAPI()\n@app.get(\"/\")\ndef read_root():"]], "corpus": "wikipedia"}
{"tstamp": 1722311095.9576, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722311095.6351, "finish": 1722311095.9576, "ip": "", "conv_id": "dd1cd051afc647eabf636fc6055dea05", "model_name": "intfloat/multilingual-e5-large-instruct", "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?", "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."]], "corpus": "wikipedia"}
{"tstamp": 1722311095.9576, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722311095.6351, "finish": 1722311095.9576, "ip": "", "conv_id": "181c322773ac4bf5a3d0c6321237b30a", "model_name": "Salesforce/SFR-Embedding-2_R", "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?", "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."]], "corpus": "wikipedia"}
{"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–industries.\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"}
{"tstamp": 1722311174.9589, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722311174.5805, "finish": 1722311174.9589, "ip": "", "conv_id": "b3eb01f72a77480caae9a4025986769f", "model_name": "voyage-multilingual-2", "prompt": "What are the differences between machine learning and deep learning?", "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."]], "corpus": "wikipedia"}
{"tstamp": 1722311209.641, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722311207.0891, "finish": 1722311209.641, "ip": "", "conv_id": "de94bd25010e43bda926db85c7971b20", "model_name": "text-embedding-3-large", "prompt": "when to use an air gap for dishwasher", "output": [["when to use an air gap for dishwasher", "Air gap (networking)\n\nAn air gap, air wall, air gapping or disconnected network is a network security measure employed on one or more computers to ensure that a secure computer network is physically isolated from unsecured networks, such as the public Internet or an unsecured local area network. It means a computer or network has no network interface controllers connected to other networks, with a physical or conceptual air gap, analogous to the air gap used in plumbing to maintain water quality."]], "corpus": "wikipedia"}
{"tstamp": 1722311209.641, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1722311207.0891, "finish": 1722311209.641, "ip": "", "conv_id": "436c2a44d65c4bb68f57a571cada2260", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "when to use an air gap for dishwasher", "output": [["when to use an air gap for dishwasher", "Air gap (networking)\n\nAn air gap, air wall, air gapping or disconnected network is a network security measure employed on one or more computers to ensure that a secure computer network is physically isolated from unsecured networks, such as the public Internet or an unsecured local area network. It means a computer or network has no network interface controllers connected to other networks, with a physical or conceptual air gap, analogous to the air gap used in plumbing to maintain water quality."]], "corpus": "wikipedia"}
{"tstamp": 1722311232.4959, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722311232.3107, "finish": 1722311232.4959, "ip": "", "conv_id": "8685880d067944afa0b8593df3edca7c", "model_name": "GritLM/GritLM-7B", "prompt": "A paper on scalable and accurate graph transformers using sparse attention and expander graphs", "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}."]], "corpus": "arxiv"}
{"tstamp": 1722311232.4959, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722311232.3107, "finish": 1722311232.4959, "ip": "", "conv_id": "e515fe1b4bad484eba5701f6c8446cc0", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "A paper on scalable and accurate graph transformers using sparse attention and expander graphs", "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."]], "corpus": "arxiv"}
{"tstamp": 1722311273.9209, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722311273.7354, "finish": 1722311273.9209, "ip": "", "conv_id": "f4c9ac94173d4247865eb70981697a10", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "What are the key principles of quantum mechanics?", "output": [["What are the key principles of quantum mechanics?", "Quantum mechanics\n\nQuantum mechanics is a fundamental theory in physics that describes the behavior of nature at and below the scale of atoms. It is the foundation of all quantum physics, which includes quantum chemistry, quantum field theory, quantum technology, and quantum information science.\nQuantum mechanics can describe many systems that classical physics cannot. Classical physics can describe many aspects of nature at an ordinary (macroscopic and (optical) microscopic) scale, but is not sufficient for describing them at very small submicroscopic (atomic and subatomic) scales. Most theories in classical physics can be derived from quantum mechanics as an approximation valid at large (macroscopic/microscopic) scale.\nQuantum systems have bound states that are quantized to discrete values of energy, momentum, angular momentum, and other quantities, in contrast to classical systems where these quantities can be measured continuously. Measurements of quantum systems show characteristics of both particles and waves (wave–particle duality), and there are limits to how accurately the value of a physical quantity can be predicted prior to its measurement, given a complete set of initial conditions (the uncertainty principle)."]], "corpus": "wikipedia"}
{"tstamp": 1722311273.9209, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1722311273.7354, "finish": 1722311273.9209, "ip": "", "conv_id": "8ed959dba71e4b10b407a68b0b7a8ca9", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "What are the key principles of quantum mechanics?", "output": [["What are the key principles of quantum mechanics?", "Introduction to quantum mechanics\n\nMany aspects of quantum mechanics are counterintuitive and can seem paradoxical because they describe behavior quite different from that seen at larger scales. In the words of quantum physicist Richard Feynman, quantum mechanics deals with \"nature as She is—absurd\". Features of quantum mechanics often defy simple explanations in everyday language. One example of this is the uncertainty principle: precise measurements of position cannot be combined with precise measurements of velocity. Another example is entanglement: a measurement made on one particle (such as an electron that is measured to have spin 'up') will correlate with a measurement on a second particle (an electron will be found to have spin 'down') if the two particles have a shared history. This will apply even if it is impossible for the result of the first measurement to have been transmitted to the second particle before the second measurement takes place.\nQuantum mechanics helps us understand chemistry, because it explains how atoms interact with each other and form molecules. Many remarkable phenomena can be explained using quantum mechanics, like superfluidity. For example, if liquid helium cooled to a temperature near absolute zero is placed in a container, it spontaneously flows up and over the rim of its container; this is an effect which cannot be explained by classical physics.\nHistory\nJames C. Maxwell's unification of the equations governing electricity, magnetism, and light in the late 19th century led to experiments on the interaction of light and matter. Some of these experiments had aspects which could not be explained until quantum mechanics emerged in the early part of the 20th century.\nEvidence of quanta from the photoelectric effect"]], "corpus": "wikipedia"}
{"tstamp": 1722311298.8755, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722311298.7354, "finish": 1722311298.8755, "ip": "", "conv_id": "e25ddbeab37a4e65805ee17fe8dbf844", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "What is the history and significance of the Silk Road?", "output": [["What is the history and significance of the Silk Road?", "Silk Road\n\nThe Silk Road was a network of Eurasian trade routes active from the second century BCE until the mid-15th century. Spanning over , it played a central role in facilitating economic, cultural, political, and religious interactions between the Eastern and Western worlds. The name \"Silk Road\" was first coined in the late 19th century, but some 20th- and 21st-century historians instead prefer the term Silk Routes, on the grounds that it more accurately describes the intricate web of land and sea routes connecting Central, East, South, Southeast, and West Asia as well as East Africa and Southern Europe."]], "corpus": "wikipedia"}
{"tstamp": 1722311298.8755, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722311298.7354, "finish": 1722311298.8755, "ip": "", "conv_id": "f903812140fb434d886b47c056c458bf", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "What is the history and significance of the Silk Road?", "output": [["What is the history and significance of the Silk Road?", "Silk Road\n\nThe Silk Road was a network of Eurasian trade routes active from the second century BCE until the mid-15th century. Spanning over , it played a central role in facilitating economic, cultural, political, and religious interactions between the Eastern and Western worlds. The name \"Silk Road\" was first coined in the late 19th century, but some 20th- and 21st-century historians instead prefer the term Silk Routes, on the grounds that it more accurately describes the intricate web of land and sea routes connecting Central, East, South, Southeast, and West Asia as well as East Africa and Southern Europe."]], "corpus": "wikipedia"}
{"tstamp": 1722311315.3188, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722311314.8171, "finish": 1722311315.3188, "ip": "", "conv_id": "c1f62d7555804ab3a153de69ca106e31", "model_name": "GritLM/GritLM-7B", "prompt": "How do black holes form and what are their main properties?", "output": [["How do black holes form and what are their main properties?", "Black hole\n\nFormation and evolution Given the bizarre character of black holes, it was long questioned whether such objects could actually exist in nature or whether they were merely pathological solutions to Einstein's equations. Einstein himself wrongly thought black holes would not form, because he held that the angular momentum of collapsing particles would stabilize their motion at some radius. This led the general relativity community to dismiss all results to the contrary for many years. However, a minority of relativists continued to contend that black holes were physical objects, and by the end of the 1960s, they had persuaded the majority of researchers in the field that there is no obstacle to the formation of an event horizon.\nPenrose demonstrated that once an event horizon forms, general relativity without quantum mechanics requires that a singularity will form within. Shortly afterwards, Hawking showed that many cosmological solutions that describe the Big Bang have singularities without scalar fields or other exotic matter. The Kerr solution, the no-hair theorem, and the laws of black hole thermodynamics showed that the physical properties of black holes were simple and comprehensible, making them respectable subjects for research. Conventional black holes are formed by gravitational collapse of heavy objects such as stars, but they can also in theory be formed by other processes."]], "corpus": "wikipedia"}
{"tstamp": 1722311315.3188, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722311314.8171, "finish": 1722311315.3188, "ip": "", "conv_id": "1c02620544ee43d78b4c741a10b183e6", "model_name": "text-embedding-3-large", "prompt": "How do black holes form and what are their main properties?", "output": [["How do black holes form and what are their main properties?", "Black hole\n\nFormation and evolution Given the bizarre character of black holes, it was long questioned whether such objects could actually exist in nature or whether they were merely pathological solutions to Einstein's equations. Einstein himself wrongly thought black holes would not form, because he held that the angular momentum of collapsing particles would stabilize their motion at some radius. This led the general relativity community to dismiss all results to the contrary for many years. However, a minority of relativists continued to contend that black holes were physical objects, and by the end of the 1960s, they had persuaded the majority of researchers in the field that there is no obstacle to the formation of an event horizon.\nPenrose demonstrated that once an event horizon forms, general relativity without quantum mechanics requires that a singularity will form within. Shortly afterwards, Hawking showed that many cosmological solutions that describe the Big Bang have singularities without scalar fields or other exotic matter. The Kerr solution, the no-hair theorem, and the laws of black hole thermodynamics showed that the physical properties of black holes were simple and comprehensible, making them respectable subjects for research. Conventional black holes are formed by gravitational collapse of heavy objects such as stars, but they can also in theory be formed by other processes."]], "corpus": "wikipedia"}
{"tstamp": 1722311338.9735, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722311338.5046, "finish": 1722311338.9735, "ip": "", "conv_id": "6db021a59686456b856a8b96bb3ccc8b", "model_name": "text-embedding-3-large", "prompt": "What are the implications of Gödel’s incompleteness theorems on the foundations of mathematics and computer science?", "output": [["What are the implications of Gödel’s incompleteness theorems on the foundations of mathematics and computer science?", "Gödel's incompleteness theorems\n\nObserve then, that if we can prove that the system  is consistent (ie. the statement in the hypothesis of ), then we have proved that  is not provable. But this is a contradiction since by the 1st Incompleteness Theorem, this sentence (ie. what is implied in the sentence , \"\"\" is not provable\") is what we construct to be unprovable. Notice that this is why we require formalizing the first Incompleteness Theorem in : to prove the 2nd Incompleteness Theorem, we obtain a contradiction with the 1st Incompleteness Theorem which can do only by showing that the theorem holds in . So we cannot prove that the system  is consistent. And the 2nd Incompleteness Theorem statement follows.\nDiscussion and implications\nThe incompleteness results affect the philosophy of mathematics, particularly versions of formalism, which use a single system of formal logic to define their principles.\nConsequences for logicism and Hilbert's second problem\nThe incompleteness theorem is sometimes thought to have severe consequences for the program of logicism proposed by Gottlob Frege and Bertrand Russell, which aimed to define the natural numbers in terms of logic. Bob Hale and Crispin Wright argue that it is not a problem for logicism because the incompleteness theorems apply equally to first-order logic as they do to arithmetic. They argue that only those who believe that the natural numbers are to be defined in terms of first order logic have this problem."]], "corpus": "wikipedia"}
{"tstamp": 1722311338.9735, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722311338.5046, "finish": 1722311338.9735, "ip": "", "conv_id": "de0a7e21ceeb42dc94708bac0371c952", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "What are the implications of Gödel’s incompleteness theorems on the foundations of mathematics and computer science?", "output": [["What are the implications of Gödel’s incompleteness theorems on the foundations of mathematics and computer science?", "Gödel's incompleteness theorems\n\nGödel's incompleteness theorems are two theorems of mathematical logic that are concerned with the limits of  in formal axiomatic theories. These results, published by Kurt Gödel in 1931, are important both in mathematical logic and in the philosophy of mathematics. The theorems are widely, but not universally, interpreted as showing that Hilbert's program to find a complete and consistent set of axioms for all mathematics is impossible.\nThe first incompleteness theorem states that no consistent system of axioms whose theorems can be listed by an effective procedure (i.e. an algorithm) is capable of proving all truths about the arithmetic of natural numbers.  For any such consistent formal system, there will always be statements about natural numbers that are true, but that are unprovable within the system.\nThe second incompleteness theorem, an extension of the first, shows that the system cannot demonstrate its own consistency.\nEmploying a diagonal argument, Gödel's incompleteness theorems were the first of several closely related theorems on the limitations of formal systems. They were followed by Tarski's undefinability theorem on the formal undefinability of truth, Church's proof that Hilbert's Entscheidungsproblem is unsolvable, and Turing's theorem that there is no algorithm to solve the halting problem.\nFormal systems: completeness, consistency, and effective axiomatization"]], "corpus": "wikipedia"}
{"tstamp": 1722313442.1056, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722313439.9312, "finish": 1722313442.1056, "ip": "", "conv_id": "4e00c146250846939c8ba7c8a40eca43", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "给我一个吻", "output": [["给我一个吻", "Taiwanese Hokkien\n\nSubject–kā–object–verb: Another sentence of roughly equivalent meaning is Goá kā lí phō, with the slight connotation of 'I take you and hold' or 'I get to you and hold'.\nObject hō͘ subject–verb (the passive voice): Then, Lí hō͘ goá phō means the same thing but in the passive voice, with the connotation of 'You allow yourself to be held by me' or 'You make yourself available for my holding'.\nWith this, more complicated sentences can be constructed: Goá hō͘ lí chúi lim ('I give water for you to drink': chúi means 'water'; lim is 'to drink').\nThis article can only give a few very simple examples on the syntax, for flavour. Linguistic work on the syntax of Taiwanese is still a (quite nascent) scholarly topic being explored.\nScripts and orthographies\nUntil the late 19th century, Taiwanese speakers wrote mostly in Classical Chinese, although songbooks using Han characters are attested from the 1820s. Among many systems of writing Taiwanese using Latin characters, the most used is called Pe̍h-ōe-jī (POJ) and was developed in the 19th century, while the Taiwanese Romanization System (Tâi-lô) has been officially promoted since 2006 by Taiwan's Ministry of Education. (For additional romanized systems, see references in \"Orthography in Latin characters\", below.) Nonetheless, Taiwanese speakers nowadays most commonly write in Mandarin, though many of the same characters are also used to write Taiwanese."]], "corpus": "wikipedia"}
{"tstamp": 1722313442.1056, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722313439.9312, "finish": 1722313442.1056, "ip": "", "conv_id": "3072a5571b6f43429edbd73072654e79", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "给我一个吻", "output": [["给我一个吻", "Kiss\n\nIn early Japanese films, kissing and sexual expression were controversial. In 1931, a director slipped a kissing scene past the censor (who was a friend), but when the film opened in a downtown Tokyo theater, the screening was stopped and the film confiscated. During the American occupation of Japan, in 1946, an American censor required a film to include a kissing scene. One scholar says that the censor suggested \"we believe that even Japanese do something like kissing when they love each other. Why don't you include that in your films?\" Americans encouraged such scenes to force the Japanese to express publicly actions and feelings that had been considered strictly private. Since Pearl Harbor, Americans had felt that the Japanese were \"sneaky\", claiming that \"if Japanese kissed in private, they should do it in public too.\"\nNon-sexual kisses\nIn some Western cultures, it is considered good luck to kiss someone on Christmas or on New Year's Eve, especially beneath a sprig of mistletoe. Newlyweds usually kiss at the end of a wedding ceremony.\nFemale friends and relations and close acquaintances commonly offer reciprocal kisses on the cheek as a greeting or farewell.\nWhere cheek kissing is used, in some countries a single kiss is the custom, while in others a kiss on each cheek is the norm, or even three or four kisses on alternating cheeks. In the United States, an air kiss is becoming more common. This involves kissing in the air near the cheek, with the cheeks touching or not. After a first date, it is common for the couple to give each other a quick kiss on the cheek (or lips where that is the norm) on parting, to indicate that a good time was had and perhaps to indicate an interest in another meeting."]], "corpus": "wikipedia"}
{"tstamp": 1722342546.8373, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722342546.2336, "finish": 1722342546.8373, "ip": "", "conv_id": "d863e942d2434ae3b85a725bc295dfa8", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "capital or france", "output": [["capital or 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."]], "corpus": "wikipedia"}
{"tstamp": 1722342546.8373, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722342546.2336, "finish": 1722342546.8373, "ip": "", "conv_id": "04e69520659e4878ab293a12b90a426c", "model_name": "text-embedding-3-large", "prompt": "capital or france", "output": [["capital or france", "List of capitals of France\n\nVersailles (1871), the French Third Republic established Versailles as its provisional seat of government in March 1871 after the Paris Commune took control of Paris.\nBordeaux (September 1914), the government was relocated from Paris to Bordeaux very briefly during World War I, when it was feared that Paris would soon fall into German hands. These fears were alleviated after the German Army was pushed back at the First Battle of the Marne.\nTours (10–13 June 1940), the city served as the temporary capital of France during World War II after the government fled Paris due to the German advance.\nBordeaux (June 1940), the government was relocated from Paris to Tours then Bordeaux very briefly during World War II, when it became apparent that Paris would soon fall into German hands.\nClermont-Ferrand (29 June 1940), the government was relocated from Bordeaux to Clermont-Ferrand, during a single day, before going to Vichy, which had a larger hotel capacity.\nVichy (1940–1944), the Third Republic was abolished in Vichy and replaced it with the French State.\nBrazzaville (1940–1943), with metropolitan France under Axis powers rule, Brazzaville was announced as the seat of the Free France government.\nAlgiers (1943–1944), the city was made the seat of Free France, to be closer to the war in Europe."]], "corpus": "wikipedia"}
{"tstamp": 1722342628.9266, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722342626.7674, "finish": 1722342628.9266, "ip": "", "conv_id": "6600474578594c0d9192933dc077d8f2", "model_name": "BM25", "prompt": "who created tensorflow", "output": [["who created tensorflow", "TensorFlow\n\nMedical GE Healthcare used TensorFlow to increase the speed and accuracy of MRIs in identifying specific body parts. Google used TensorFlow to create DermAssist, a free mobile application that allows users to take pictures of their skin and identify potential health complications. Sinovation Ventures used TensorFlow to identify and classify eye diseases from optical coherence tomography (OCT) scans.\nSocial media\nTwitter implemented TensorFlow to rank tweets by importance for a given user, and changed their platform to show tweets in order of this ranking. Previously, tweets were simply shown in reverse chronological order. The photo sharing app VSCO used TensorFlow to help suggest custom filters for photos.\nSearch Engine\nGoogle officially released RankBrain on October 26, 2015, backed by TensorFlow.\nEducation\nInSpace, a virtual learning platform, used TensorFlow to filter out toxic chat messages in classrooms. Liulishuo, an online English learning platform, utilized TensorFlow to create an adaptive curriculum for each student. TensorFlow was used to accurately assess a student's current abilities, and also helped decide the best future content to show based on those capabilities.\nRetail\nThe e-commerce platform Carousell used TensorFlow to provide personalized recommendations for customers. The cosmetics company ModiFace used TensorFlow to create an augmented reality experience for customers to test various shades of make-up on their face.\nResearch\nTensorFlow is the foundation for the automated image-captioning software DeepDream."]], "corpus": "wikipedia"}
{"tstamp": 1722342628.9266, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722342626.7674, "finish": 1722342628.9266, "ip": "", "conv_id": "565cdcfdfa6846e2a40cb0dd2b64a586", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "who created tensorflow", "output": [["who created tensorflow", "TensorFlow\n\nIn May 2019, Google announced TensorFlow Graphics for deep learning in computer graphics.\nTensor processing unit (TPU)\nIn May 2016, Google announced its Tensor processing unit (TPU), an application-specific integrated circuit (ASIC, a hardware chip) built specifically for machine learning and tailored for TensorFlow. A TPU is a programmable AI accelerator designed to provide high throughput of low-precision arithmetic (e.g., 8-bit), and oriented toward using or running models rather than training them. Google announced they had been running TPUs inside their data centers for more than a year, and had found them to deliver an order of magnitude better-optimized performance per watt for machine learning.\nIn May 2017, Google announced the second-generation, as well as the availability of the TPUs in Google Compute Engine. The second-generation TPUs deliver up to 180 teraflops of performance, and when organized into clusters of 64 TPUs, provide up to 11.5 petaflops.\nIn May 2018, Google announced the third-generation TPUs delivering up to 420 teraflops of performance and 128 GB high bandwidth memory (HBM). Cloud TPU v3 Pods offer 100+ petaflops of performance and 32 TB HBM.\nIn February 2018, Google announced that they were making TPUs available in beta on the Google Cloud Platform."]], "corpus": "wikipedia"}