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data/clustering_individual-2b220830-844b-4128-a00a-03ab29c3b3ac.jsonl CHANGED
@@ -20,3 +20,17 @@
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data/retrieval_individual-2b220830-844b-4128-a00a-03ab29c3b3ac.jsonl CHANGED
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  {"tstamp": 1730795769.5872, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1730795769.3881, "finish": 1730795769.5872, "ip": "", "conv_id": "9ab04236d15442658bfa0e298a8b8c50", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "A transformer model that incorporates ideas from long-input transformers and PEGASUS to improve performance.", "output": [["A transformer model that incorporates ideas from long-input transformers and PEGASUS to improve performance.", "Title: A Practical Survey on Faster and Lighter Transformers\n\nAbstract: Recurrent neural networks are effective models to process sequences. However, they are unable to learn long-term dependencies because of their inherent sequential nature. As a solution, Vaswani et al. introduced the Transformer, a model solely based on the attention mechanism that is able to relate any two positions of the input sequence, hence modelling arbitrary long dependencies. The Transformer has improved the state-of-the-art across numerous sequence modelling tasks. However, its effectiveness comes at the expense of a quadratic computational and memory complexity with respect to the sequence length, hindering its adoption. Fortunately, the deep learning community has always been interested in improving the models' efficiency, leading to a plethora of solutions such as parameter sharing, pruning, mixed-precision, and knowledge distillation. Recently, researchers have directly addressed the Transformer's limitation by designing lower-complexity alternatives such as the Longformer, Reformer, Linformer, and Performer. However, due to the wide range of solutions, it has become challenging for researchers and practitioners to determine which methods to apply in practice in order to meet the desired trade-off between capacity, computation, and memory. This survey addresses this issue by investigating popular approaches to make Transformers faster and lighter and by providing a comprehensive explanation of the methods' strengths, limitations, and underlying assumptions."]], "corpus": "arxiv"}
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  {"tstamp": 1730795811.5272, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1730795809.3486, "finish": 1730795811.5272, "ip": "", "conv_id": "a17c936785f447a7adc44b26c6983090", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "corpus": "wikipedia"}
102
  {"tstamp": 1730795811.5272, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1730795809.3486, "finish": 1730795811.5272, "ip": "", "conv_id": "1cf97de29c35479181015257bc798e1d", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Artificial general intelligence\n\nMany interdisciplinary approaches (e.g. cognitive science, computational intelligence, and decision making) consider additional traits such as imagination (the ability to form novel mental images and concepts) and autonomy.\nComputer-based systems that exhibit many of these capabilities exist (e.g. see computational creativity, automated reasoning, decision support system, robot, evolutionary computation, intelligent agent). However, no consensus holds that modern AI systems possess them to an adequate degree.\nPhysical traits\nOther capabilities are considered desirable in intelligent systems, as they may affect intelligence or aid in its expression. These include:\nthe ability to sense (e.g. see, hear, etc.), and\nthe ability to act (e.g. move and manipulate objects, change location to explore, etc.)\nThis includes the ability to detect and respond to hazard.\nTests for human-level AGI\nSeveral tests meant to confirm human-level AGI have been considered, including:\nThe Turing Test (Turing)\nA machine and a human both converse unseen with a second human, who must evaluate which of the two is the machine, which passes the test if it can fool the evaluator a significant fraction of the time. Note: Turing does not prescribe what should qualify as intelligence, only that knowing that it is a machine should disqualify it. The AI Eugene Goostman, imitating a 13-year-old boy, achieved Turing's estimate of convincing 33% of judges that it was human in 2014.\nThe Robot College Student Test (Goertzel)"]], "corpus": "wikipedia"}
 
 
100
  {"tstamp": 1730795769.5872, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1730795769.3881, "finish": 1730795769.5872, "ip": "", "conv_id": "9ab04236d15442658bfa0e298a8b8c50", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "A transformer model that incorporates ideas from long-input transformers and PEGASUS to improve performance.", "output": [["A transformer model that incorporates ideas from long-input transformers and PEGASUS to improve performance.", "Title: A Practical Survey on Faster and Lighter Transformers\n\nAbstract: Recurrent neural networks are effective models to process sequences. However, they are unable to learn long-term dependencies because of their inherent sequential nature. As a solution, Vaswani et al. introduced the Transformer, a model solely based on the attention mechanism that is able to relate any two positions of the input sequence, hence modelling arbitrary long dependencies. The Transformer has improved the state-of-the-art across numerous sequence modelling tasks. However, its effectiveness comes at the expense of a quadratic computational and memory complexity with respect to the sequence length, hindering its adoption. Fortunately, the deep learning community has always been interested in improving the models' efficiency, leading to a plethora of solutions such as parameter sharing, pruning, mixed-precision, and knowledge distillation. Recently, researchers have directly addressed the Transformer's limitation by designing lower-complexity alternatives such as the Longformer, Reformer, Linformer, and Performer. However, due to the wide range of solutions, it has become challenging for researchers and practitioners to determine which methods to apply in practice in order to meet the desired trade-off between capacity, computation, and memory. This survey addresses this issue by investigating popular approaches to make Transformers faster and lighter and by providing a comprehensive explanation of the methods' strengths, limitations, and underlying assumptions."]], "corpus": "arxiv"}
101
  {"tstamp": 1730795811.5272, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1730795809.3486, "finish": 1730795811.5272, "ip": "", "conv_id": "a17c936785f447a7adc44b26c6983090", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "corpus": "wikipedia"}
102
  {"tstamp": 1730795811.5272, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1730795809.3486, "finish": 1730795811.5272, "ip": "", "conv_id": "1cf97de29c35479181015257bc798e1d", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Artificial general intelligence\n\nMany interdisciplinary approaches (e.g. cognitive science, computational intelligence, and decision making) consider additional traits such as imagination (the ability to form novel mental images and concepts) and autonomy.\nComputer-based systems that exhibit many of these capabilities exist (e.g. see computational creativity, automated reasoning, decision support system, robot, evolutionary computation, intelligent agent). However, no consensus holds that modern AI systems possess them to an adequate degree.\nPhysical traits\nOther capabilities are considered desirable in intelligent systems, as they may affect intelligence or aid in its expression. These include:\nthe ability to sense (e.g. see, hear, etc.), and\nthe ability to act (e.g. move and manipulate objects, change location to explore, etc.)\nThis includes the ability to detect and respond to hazard.\nTests for human-level AGI\nSeveral tests meant to confirm human-level AGI have been considered, including:\nThe Turing Test (Turing)\nA machine and a human both converse unseen with a second human, who must evaluate which of the two is the machine, which passes the test if it can fool the evaluator a significant fraction of the time. Note: Turing does not prescribe what should qualify as intelligence, only that knowing that it is a machine should disqualify it. The AI Eugene Goostman, imitating a 13-year-old boy, achieved Turing's estimate of convincing 33% of judges that it was human in 2014.\nThe Robot College Student Test (Goertzel)"]], "corpus": "wikipedia"}
103
+ {"tstamp": 1730814404.8765, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1730814402.5675, "finish": 1730814404.8765, "ip": "", "conv_id": "90e79d0bbfcd46e89440af9569f58dda", "model_name": "GritLM/GritLM-7B", "prompt": "\nScientists achieved quantum entanglement between particles separated by a record distance. <|SEP|>\nThe new quantum computer solved a complex problem in mere seconds. <|SEP|>\nResearchers observed a novel quantum phenomenon in superconducting materials. <|SEP|>\nThe chef combined molecular gastronomy techniques with traditional French cuisine. <|SEP|>\nA rare variety of truffles was discovered in an unexpected region of Italy. <|SEP|>\nThe annual food festival showcased fusion dishes from around the world. <|SEP|>\nFarmers implemented vertical farming methods to maximize urban food production. <|SEP|>\nA new drought-resistant strain of wheat showed promising results in field trials. <|SEP|>\nThe organic farm used beneficial insects to control pests without chemicals. <|SEP|>\nResearchers developed a more efficient irrigation system for arid climates.\n", "output": [["\nScientists achieved quantum entanglement between particles separated by a record distance. <|SEP|>\nThe new quantum computer solved a complex problem in mere seconds. <|SEP|>\nResearchers observed a novel quantum phenomenon in superconducting materials. <|SEP|>\nThe chef combined molecular gastronomy techniques with traditional French cuisine. <|SEP|>\nA rare variety of truffles was discovered in an unexpected region of Italy. <|SEP|>\nThe annual food festival showcased fusion dishes from around the world. <|SEP|>\nFarmers implemented vertical farming methods to maximize urban food production. <|SEP|>\nA new drought-resistant strain of wheat showed promising results in field trials. <|SEP|>\nThe organic farm used beneficial insects to control pests without chemicals. <|SEP|>\nResearchers developed a more efficient irrigation system for arid climates.\n", "Quantum computing\n\nIn December 2023, physicists, for the first time, report the entanglement of individual molecules, which may have significant applications in quantum computing. Also in December 2023, scientists at Harvard University successfully created \"quantum circuits\" that correct errors more efficiently than alternative methods, which may potentially remove a major obstacle to practical quantum computers. The Harvard research team was supported by MIT, QuEra Computing, Caltech, and Princeton University and funded by DARPA's Optimization with Noisy Intermediate-Scale Quantum devices (ONISQ) program. Research efforts are ongoing to jumpstart quantum computing through topological and photonic approaches as well.\nIn July 2024, quantum computing company Quantinuum announced that their new 56-qubit H2-1 computer has broken a world record in \"quantum supremacy,\" topping the performance of benchmarking set by Google's Sycamore machine by 100-fold.\nQuantum information processing\nComputer engineers typically describe a modern computer's operation in terms of classical electrodynamics.\nWithin these \"classical\" computers, some components (such as semiconductors and random number generators) may rely on quantum behavior, but these components are not isolated from their environment, so any quantum information quickly decoheres.\nWhile programmers may depend on probability theory when designing a randomized algorithm, quantum mechanical notions like superposition and interference are largely irrelevant for program analysis."]], "corpus": "wikipedia"}