Scheduled Commit

data/retrieval_battle-a660d6d7-6b46-4e6b-9c85-4651683faa00.jsonl

@@ -33,3 +33,5 @@
 {"tstamp": 1726319492.1736, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "0a126fd2f4884f3f96e04f20645482de", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "Progress in artificial intelligence\n\nSub-human Optical character recognition for printed text (nearing par-human for Latin-script typewritten text)\nObject recognition\nVarious robotics tasks that may require advances in robot hardware as well as AI, including:\nStable bipedal locomotion: Bipedal robots can walk, but are less stable than human walkers (as of 2017)\nHumanoid soccer\nSpeech recognition: \"nearly equal to human performance\" (2017)\nExplainability. Current medical systems can diagnose certain medical conditions well, but cannot explain to users why they made the diagnosis.\nMany tests of fluid intelligence (2020)\nBongard visual cognition problems, such as the Bongard-LOGO benchmark (2020)\nVisual Commonsense Reasoning (VCR) benchmark (as of 2020)\nStock market prediction: Financial data collection and processing using Machine Learning algorithms\nAngry Birds video game, as of 2020\nVarious tasks that are difficult to solve without contextual knowledge, including:\nTranslation\nWord-sense disambiguation\nProposed tests of artificial intelligence\nIn his famous Turing test, Alan Turing picked language, the defining feature of human beings, for its basis. The Turing test is now considered too exploitable to be a meaningful benchmark.\nThe Feigenbaum test, proposed by the inventor of expert systems, tests a machine's knowledge and expertise about a specific subject. A paper by Jim Gray of Microsoft in 2003 suggested extending the Turing test to speech understanding, speaking and recognizing objects and behavior."]], "0_corpus": "wikipedia", "1_conv_id": "cb6a56a042354fd08577d0f1a95d72d0", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "Which test was devised to determine whether robots can think?", "1_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "1_corpus": "wikipedia"}
 {"tstamp": 1726346126.967, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "b2b3b827c3914f26a1180a4f3c7209e7", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe test was introduced by Turing in his 1950 paper \"Computing Machinery and Intelligence\" while working at the University of Manchester. It opens with the words: \"I propose to consider the question, 'Can machines think? Because \"thinking\" is difficult to define, Turing chooses to \"replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\" Turing describes the new form of the problem in terms of a three-person game called the \"imitation game\", in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: \"Are there imaginable digital computers which would do well in the imitation game?\" This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that \"machines can think\".\nSince Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a \"mind\", \"understanding\", or \"consciousness\", regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.\nHistory"]], "0_corpus": "wikipedia", "1_conv_id": "70fafc3532634e5694a9403fbf40051d", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "Which test was devised to determine whether robots can think?", "1_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "1_corpus": "wikipedia"}
 {"tstamp": 1726346264.4223, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "cfdfed2129cf416eb8309fff16dfd4f6", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": "Why did the machines in The Matrix keep humans around?", "0_output": [["Why did the machines in The Matrix keep humans around?", "Scifi Stackexchange\n\nQ: Why use full brained humans as batteries? \nPossible Duplicate:\nIs the basic premise of humans as a power source in The Matrix reasonable?\nWhy Did the Machines Even Bother With a Matrix? \n\nThe machines in The Matrix series use humans as batteries. That's OK. But why did the machines not alter their livestock such that the matrix itself would be unnecessary?  \nConsider the recently proposed headless chicken farm. Something similar for the human batteries would seem to be an ideal solution for logical machines. No longer would they need to spend resources maintaining a virtual reality for their batteries. The energy, physical hardware and programs running the matrix could then be put to who knows what other uses.\n\nA: The explanation for the use of human brains would seem to be the fact that our complex thoughts lead to higher level of brain activity, thus resulting in exponentially more synapses firing (i.e. more electrical activity to harvest).\nThat makes human brains far more efficient than the brains of animals, even with the overhead of maintaining the matrix.  Plus, that movie would have really sucked... dumb cows and chickens revolt against the machines!  ;)\n"]], "0_corpus": "stackexchange", "1_conv_id": "3d48af9035c2483cae04c64168f9c4c2", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "Why did the machines in The Matrix keep humans around?", "1_output": [["Why did the machines in The Matrix keep humans around?", "Scifi Stackexchange\n\nQ: Do the Machines in \"The Matrix\" Adhere to the Three Laws of Robotics? In the \"real world\" of the Matrix, do the machines follow Asimov's Three Rules? Were they even programmed with them?\nEDIT: I realize that killing people goes against the 1st law, but by keeping people in the Matrix (which the machines view as a symbiosis) rather than simply killing off these \"annoying\" creatures, I wonder if the killings can be seen as \"preserving the greater good\" or something along those lines; and thus justify violating the laws.\n\nA: \nA robot may not injure a human being or, through inaction, allow a\n  human being to come to harm.\nA robot must obey the orders given to it by human beings, except where\n  such orders would conflict with the First Law.\nA robot must protect its own existence as long as such protection does\n  not conflict with the First or Second Laws.\n\nNo, no, and no.\n"]], "1_corpus": "stackexchange"}
+{"tstamp": 1726347134.5622, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "6e1f60d379b2407f820b053f9d08247d", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "Retrieve the latest paper on arXiv about CRISPR", "0_output": [["Retrieve the latest paper on arXiv about CRISPR", "Title: The physicist's guide to one of biotechnology's hottest new topics: CRISPR-Cas\n\nAbstract: Clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated proteins (Cas) constitute a multi-functional, constantly evolving immune system in bacteria and archaea cells. A heritable, molecular memory is generated of phage, plasmids, or other mobile genetic elements that attempt to attack the cell. This memory is used to recognize and interfere with subsequent invasions from the same genetic elements. This versatile prokaryotic tool has also been used to advance applications in biotechnology. Here we review a large body of CRISPR-Cas research to explore themes of evolution and selection, population dynamics, horizontal gene transfer, specific and cross-reactive interactions, cost and regulation, non-immunological CRISPR functions that boost host cell robustness, as well as applicable mechanisms for efficient and specific genetic engineering. We offer future directions that can be addressed by the physics community. Physical understanding of the CRISPR-Cas system will advance uses in biotechnology, such as developing cell lines and animal models, cell labeling and information storage, combatting antibiotic resistance, and human therapeutics."]], "0_corpus": "arxiv", "1_conv_id": "61d73adaa4964fe0b44e33b7c37228db", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "Retrieve the latest paper on arXiv about CRISPR", "1_output": [["Retrieve the latest paper on arXiv about CRISPR", "Title: Preprint D\\'ej\\`a Vu: an FAQ\n\nAbstract: I give a brief overview of arXiv history, and describe the current state of arXiv practice, both technical and sociological. This commentary originally appeared in the EMBO Journal, 19 Oct 2016. It was intended as an update on comments from the late 1990s regarding use of preprints by biologists (or lack thereof), but may be of interest to practitioners of other disciplines. It is based largely on a keynote presentation I gave to the ASAPbio inaugural meeting in Feb 2016, and responds as well to some follow-up questions."]], "1_corpus": "arxiv"}
+{"tstamp": 1726347221.4165, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "1dd389690ec1432e8ea5c0fb24a4cf3c", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "Find two papers on gradient descent that present the opposite conclusions ", "0_output": [["Find two papers on gradient descent that present the opposite conclusions ", "Title: Gradient Descent can Learn Less Over-parameterized Two-layer Neural Networks on Classification Problems\n\nAbstract: Recently, several studies have proven the global convergence and generalization abilities of the gradient descent method for two-layer ReLU networks. Most studies especially focused on the regression problems with the squared loss function, except for a few, and the importance of the positivity of the neural tangent kernel has been pointed out. On the other hand, the performance of gradient descent on classification problems using the logistic loss function has not been well studied, and further investigation of this problem structure is possible. In this work, we demonstrate that the separability assumption using a neural tangent model is more reasonable than the positivity condition of the neural tangent kernel and provide a refined convergence analysis of the gradient descent for two-layer networks with smooth activations. A remarkable point of our result is that our convergence and generalization bounds have much better dependence on the network width in comparison to related studies. Consequently, our theory provides a generalization guarantee for less over-parameterized two-layer networks, while most studies require much higher over-parameterization."]], "0_corpus": "arxiv", "1_conv_id": "266f7a34b46b4633a3bfc985c6d45502", "1_model_name": "intfloat/multilingual-e5-large-instruct", "1_prompt": "Find two papers on gradient descent that present the opposite conclusions ", "1_output": [["Find two papers on gradient descent that present the opposite conclusions ", "Title: Non-approximability of constructive global $\\mathcal{L}^2$ minimizers by gradient descent in Deep Learning\n\nAbstract: We analyze geometric aspects of the gradient descent algorithm in Deep Learning (DL) networks. In particular, we prove that the globally minimizing weights and biases for the $\\mathcal{L}^2$ cost obtained constructively in [Chen-Munoz Ewald 2023] for underparametrized ReLU DL networks can generically not be approximated via the gradient descent flow. We therefore conclude that the method introduced in [Chen-Munoz Ewald 2023] is disjoint from the gradient descent method."]], "1_corpus": "arxiv"}

data/retrieval_individual-a660d6d7-6b46-4e6b-9c85-4651683faa00.jsonl

@@ -132,3 +132,7 @@
 {"tstamp": 1726346178.1793, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1726346175.905, "finish": 1726346178.1793, "ip": "", "conv_id": "918b27eeeef3436b87f7a53bbcae49c6", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "In English when to use –, — vs -?", "output": [["In English when to use –, — vs -?", "English Stackexchange\n\nQ: When to use -, – and —? \nPossible Duplicate:\nWhen should I use an em-dash, an en-dash, and a hyphen? \n\nThis is about hyphens (-), en-dashes (–) and em-dashes (—).\nWhen to use which one? To be honest, I always use em-dashes unless I join words with a hyphen, but I never use an en-dash.\n\nA: Hyphens are used in compound modifiers.\n\n\n*\n\n*a well-trained professional\n\n*over-the-counter drugs\n\n\nEn dashes are used in place of \"to\" to connect numbers or words.\n\n\n*\n\n*The London–Amsterdam flight is delayed.\n\n*The meeting is from 3:00–4:30 p.m.\n\n\nEm dashes help set off amplifying or explanatory statements.\n\n\n*\n\n*The vampire—it had been awake half the night—flew out of its coffin.\n\n"]], "corpus": "stackexchange"}
 {"tstamp": 1726346221.303, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1726346219.1423, "finish": 1726346221.303, "ip": "", "conv_id": "cfdfed2129cf416eb8309fff16dfd4f6", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "Why did the machines in The Matrix keep humans around?", "output": [["Why did the machines in The Matrix keep humans around?", "Scifi Stackexchange\n\nQ: Why use full brained humans as batteries? \nPossible Duplicate:\nIs the basic premise of humans as a power source in The Matrix reasonable?\nWhy Did the Machines Even Bother With a Matrix? \n\nThe machines in The Matrix series use humans as batteries. That's OK. But why did the machines not alter their livestock such that the matrix itself would be unnecessary?  \nConsider the recently proposed headless chicken farm. Something similar for the human batteries would seem to be an ideal solution for logical machines. No longer would they need to spend resources maintaining a virtual reality for their batteries. The energy, physical hardware and programs running the matrix could then be put to who knows what other uses.\n\nA: The explanation for the use of human brains would seem to be the fact that our complex thoughts lead to higher level of brain activity, thus resulting in exponentially more synapses firing (i.e. more electrical activity to harvest).\nThat makes human brains far more efficient than the brains of animals, even with the overhead of maintaining the matrix.  Plus, that movie would have really sucked... dumb cows and chickens revolt against the machines!  ;)\n"]], "corpus": "stackexchange"}
 {"tstamp": 1726346221.303, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1726346219.1423, "finish": 1726346221.303, "ip": "", "conv_id": "3d48af9035c2483cae04c64168f9c4c2", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "Why did the machines in The Matrix keep humans around?", "output": [["Why did the machines in The Matrix keep humans around?", "Scifi Stackexchange\n\nQ: Do the Machines in \"The Matrix\" Adhere to the Three Laws of Robotics? In the \"real world\" of the Matrix, do the machines follow Asimov's Three Rules? Were they even programmed with them?\nEDIT: I realize that killing people goes against the 1st law, but by keeping people in the Matrix (which the machines view as a symbiosis) rather than simply killing off these \"annoying\" creatures, I wonder if the killings can be seen as \"preserving the greater good\" or something along those lines; and thus justify violating the laws.\n\nA: \nA robot may not injure a human being or, through inaction, allow a\n  human being to come to harm.\nA robot must obey the orders given to it by human beings, except where\n  such orders would conflict with the First Law.\nA robot must protect its own existence as long as such protection does\n  not conflict with the First or Second Laws.\n\nNo, no, and no.\n"]], "corpus": "stackexchange"}
+{"tstamp": 1726347121.3427, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1726347119.1314, "finish": 1726347121.3427, "ip": "", "conv_id": "6e1f60d379b2407f820b053f9d08247d", "model_name": "GritLM/GritLM-7B", "prompt": "Retrieve the latest paper on arXiv about CRISPR", "output": [["Retrieve the latest paper on arXiv about CRISPR", "Title: The physicist's guide to one of biotechnology's hottest new topics: CRISPR-Cas\n\nAbstract: Clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated proteins (Cas) constitute a multi-functional, constantly evolving immune system in bacteria and archaea cells. A heritable, molecular memory is generated of phage, plasmids, or other mobile genetic elements that attempt to attack the cell. This memory is used to recognize and interfere with subsequent invasions from the same genetic elements. This versatile prokaryotic tool has also been used to advance applications in biotechnology. Here we review a large body of CRISPR-Cas research to explore themes of evolution and selection, population dynamics, horizontal gene transfer, specific and cross-reactive interactions, cost and regulation, non-immunological CRISPR functions that boost host cell robustness, as well as applicable mechanisms for efficient and specific genetic engineering. We offer future directions that can be addressed by the physics community. Physical understanding of the CRISPR-Cas system will advance uses in biotechnology, such as developing cell lines and animal models, cell labeling and information storage, combatting antibiotic resistance, and human therapeutics."]], "corpus": "arxiv"}
+{"tstamp": 1726347121.3427, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1726347119.1314, "finish": 1726347121.3427, "ip": "", "conv_id": "61d73adaa4964fe0b44e33b7c37228db", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "Retrieve the latest paper on arXiv about CRISPR", "output": [["Retrieve the latest paper on arXiv about CRISPR", "Title: Preprint D\\'ej\\`a Vu: an FAQ\n\nAbstract: I give a brief overview of arXiv history, and describe the current state of arXiv practice, both technical and sociological. This commentary originally appeared in the EMBO Journal, 19 Oct 2016. It was intended as an update on comments from the late 1990s regarding use of preprints by biologists (or lack thereof), but may be of interest to practitioners of other disciplines. It is based largely on a keynote presentation I gave to the ASAPbio inaugural meeting in Feb 2016, and responds as well to some follow-up questions."]], "corpus": "arxiv"}
+{"tstamp": 1726347194.4547, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1726347192.2608, "finish": 1726347194.4547, "ip": "", "conv_id": "1dd389690ec1432e8ea5c0fb24a4cf3c", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "Find two papers on gradient descent that present the opposite conclusions ", "output": [["Find two papers on gradient descent that present the opposite conclusions ", "Title: Gradient Descent can Learn Less Over-parameterized Two-layer Neural Networks on Classification Problems\n\nAbstract: Recently, several studies have proven the global convergence and generalization abilities of the gradient descent method for two-layer ReLU networks. Most studies especially focused on the regression problems with the squared loss function, except for a few, and the importance of the positivity of the neural tangent kernel has been pointed out. On the other hand, the performance of gradient descent on classification problems using the logistic loss function has not been well studied, and further investigation of this problem structure is possible. In this work, we demonstrate that the separability assumption using a neural tangent model is more reasonable than the positivity condition of the neural tangent kernel and provide a refined convergence analysis of the gradient descent for two-layer networks with smooth activations. A remarkable point of our result is that our convergence and generalization bounds have much better dependence on the network width in comparison to related studies. Consequently, our theory provides a generalization guarantee for less over-parameterized two-layer networks, while most studies require much higher over-parameterization."]], "corpus": "arxiv"}
+{"tstamp": 1726347194.4547, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1726347192.2608, "finish": 1726347194.4547, "ip": "", "conv_id": "266f7a34b46b4633a3bfc985c6d45502", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "Find two papers on gradient descent that present the opposite conclusions ", "output": [["Find two papers on gradient descent that present the opposite conclusions ", "Title: Non-approximability of constructive global $\\mathcal{L}^2$ minimizers by gradient descent in Deep Learning\n\nAbstract: We analyze geometric aspects of the gradient descent algorithm in Deep Learning (DL) networks. In particular, we prove that the globally minimizing weights and biases for the $\\mathcal{L}^2$ cost obtained constructively in [Chen-Munoz Ewald 2023] for underparametrized ReLU DL networks can generically not be approximated via the gradient descent flow. We therefore conclude that the method introduced in [Chen-Munoz Ewald 2023] is disjoint from the gradient descent method."]], "corpus": "arxiv"}