Scheduled Commit

data/retrieval_battle-1b2a6102-80bb-417d-a081-cf780f49c66c.jsonl

@@ -302,3 +302,4 @@
 {"tstamp": 1739466677.8438, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "db76233e81824cd08027e3698fa03976", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": "financial sentiment analysis", "0_output": [["financial sentiment analysis", "Title: Sentiment Analysis of Financial News Articles using Performance Indicators\n\nAbstract: Mining financial text documents and understanding the sentiments of individual investors, institutions and markets is an important and challenging problem in the literature. Current approaches to mine sentiments from financial texts largely rely on domain specific dictionaries. However, dictionary based methods often fail to accurately predict the polarity of financial texts. This paper aims to improve the state-of-the-art and introduces a novel sentiment analysis approach that employs the concept of financial and non-financial performance indicators. It presents an association rule mining based hierarchical sentiment classifier model to predict the polarity of financial texts as positive, neutral or negative. The performance of the proposed model is evaluated on a benchmark financial dataset. The model is also compared against other state-of-the-art dictionary and machine learning based approaches and the results are found to be quite promising. The novel use of performance indicators for financial sentiment analysis offers interesting and useful insights."]], "0_corpus": "arxiv", "1_conv_id": "ddc239e4d86140c2863a123906438654", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "financial sentiment analysis", "1_output": [["financial sentiment analysis", "Title: Sentiment Analysis of Financial News Articles using Performance Indicators\n\nAbstract: Mining financial text documents and understanding the sentiments of individual investors, institutions and markets is an important and challenging problem in the literature. Current approaches to mine sentiments from financial texts largely rely on domain specific dictionaries. However, dictionary based methods often fail to accurately predict the polarity of financial texts. This paper aims to improve the state-of-the-art and introduces a novel sentiment analysis approach that employs the concept of financial and non-financial performance indicators. It presents an association rule mining based hierarchical sentiment classifier model to predict the polarity of financial texts as positive, neutral or negative. The performance of the proposed model is evaluated on a benchmark financial dataset. The model is also compared against other state-of-the-art dictionary and machine learning based approaches and the results are found to be quite promising. The novel use of performance indicators for financial sentiment analysis offers interesting and useful insights."]], "1_corpus": "arxiv"}
 {"tstamp": 1739478848.5189, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "e6680d3cb3d54146891a33ea494e4c8f", "0_model_name": "text-embedding-004", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "0_corpus": "wikipedia", "1_conv_id": "b7b7a087733a4b8c905665488481e1d2", "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?", "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)"]], "1_corpus": "wikipedia"}
 {"tstamp": 1739481014.9074, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "71ad4569bd884ed19d0e6e944c3be8d4", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": "what does it mean when someone says sure?", "0_output": [["what does it mean when someone says sure?", "Ell Stackexchange\n\nQ: Is it OK to respond to \"thanks\" with \"sure\"? When someone says\n\n..., thanks.\n\nIs it appropriate to respond with\n\nsure\n\nIf so, in which situations it would be OK, and what exactly would it imply?\n\nA: It is a perfectly normal response that people will understand if you say it to them. I say of course when someone says Thank you, and I see that as a normal response also. The same with no problem, you're welcome, yes, etc. I see it is as a much better response than receiving no response at all.\n"]], "0_corpus": "stackexchange", "1_conv_id": "aa4f0f6f6f4a457985f9c91b71a26da7", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_prompt": "what does it mean when someone says sure?", "1_output": [["what does it mean when someone says sure?", "English Stackexchange\n\nQ: Can \"Sure\" be used to respond to \"Thanks\"? I often hear \"Sure\" in response when I say \"Thank you\" or \"Thanks\" to someone. I don't know — is this correct usage? If it is considered good, I'll use it someday.\n\nA: Yes, in the US at least. In the UK, it's unusual, on the other hand.\n"]], "1_corpus": "stackexchange"}
+{"tstamp": 1739484507.1317, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "7ca1a08bd6124a6d8efbc68cb228bb0e", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "Cognitive test\n\nCognitive tests are assessments of the cognitive capabilities of humans and other animals. Tests administered to humans include various forms of IQ tests; those administered to animals include the mirror test (a test of visual self-awareness) and the T maze test (which tests learning ability). Such testing is used in psychology and psychometrics, as well as other fields studying human and animal intelligence."]], "0_corpus": "wikipedia", "1_conv_id": "9420d01e41cf4e4e90c97b09181b5304", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "Which test was devised to determine whether robots can think?", "1_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "1_corpus": "wikipedia"}

data/retrieval_individual-1b2a6102-80bb-417d-a081-cf780f49c66c.jsonl

@@ -1125,3 +1125,11 @@
 {"tstamp": 1739480897.9709, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1739480895.7817, "finish": 1739480897.9709, "ip": "", "conv_id": "aa4f0f6f6f4a457985f9c91b71a26da7", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "what does it mean when someone says sure?", "output": [["what does it mean when someone says sure?", "English Stackexchange\n\nQ: Can \"Sure\" be used to respond to \"Thanks\"? I often hear \"Sure\" in response when I say \"Thank you\" or \"Thanks\" to someone. I don't know — is this correct usage? If it is considered good, I'll use it someday.\n\nA: Yes, in the US at least. In the UK, it's unusual, on the other hand.\n"]], "corpus": "stackexchange"}
 {"tstamp": 1739483787.8573, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1739483785.545, "finish": 1739483787.8573, "ip": "", "conv_id": "e182e26efd0d4a6ab3bd47ece9ce7438", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Progress in artificial intelligence\n\nSub-human Optical character recognition for printed text (nearing par-human for Latin-script typewritten text)\nObject recognition\nVarious robotics tasks that may require advances in robot hardware as well as AI, including:\nStable bipedal locomotion: Bipedal robots can walk, but are less stable than human walkers (as of 2017)\nHumanoid soccer\nSpeech recognition: \"nearly equal to human performance\" (2017)\nExplainability. Current medical systems can diagnose certain medical conditions well, but cannot explain to users why they made the diagnosis.\nMany tests of fluid intelligence (2020)\nBongard visual cognition problems, such as the Bongard-LOGO benchmark (2020)\nVisual Commonsense Reasoning (VCR) benchmark (as of 2020)\nStock market prediction: Financial data collection and processing using Machine Learning algorithms\nAngry Birds video game, as of 2020\nVarious tasks that are difficult to solve without contextual knowledge, including:\nTranslation\nWord-sense disambiguation\nProposed tests of artificial intelligence\nIn his famous Turing test, Alan Turing picked language, the defining feature of human beings, for its basis. The Turing test is now considered too exploitable to be a meaningful benchmark.\nThe Feigenbaum test, proposed by the inventor of expert systems, tests a machine's knowledge and expertise about a specific subject. A paper by Jim Gray of Microsoft in 2003 suggested extending the Turing test to speech understanding, speaking and recognizing objects and behavior."]], "corpus": "wikipedia"}
 {"tstamp": 1739483787.8573, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1739483785.545, "finish": 1739483787.8573, "ip": "", "conv_id": "8402c865852c4b9189e926e76304dac0", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "corpus": "wikipedia"}
+{"tstamp": 1739484491.4483, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1739484489.1302, "finish": 1739484491.4483, "ip": "", "conv_id": "7ca1a08bd6124a6d8efbc68cb228bb0e", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Cognitive test\n\nCognitive tests are assessments of the cognitive capabilities of humans and other animals. Tests administered to humans include various forms of IQ tests; those administered to animals include the mirror test (a test of visual self-awareness) and the T maze test (which tests learning ability). Such testing is used in psychology and psychometrics, as well as other fields studying human and animal intelligence."]], "corpus": "wikipedia"}
+{"tstamp": 1739484491.4483, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1739484489.1302, "finish": 1739484491.4483, "ip": "", "conv_id": "9420d01e41cf4e4e90c97b09181b5304", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "corpus": "wikipedia"}
+{"tstamp": 1739484613.2267, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1739484611.0821, "finish": 1739484613.2267, "ip": "", "conv_id": "41e7a47d3cc44868966615c632bf2c3f", "model_name": "text-embedding-3-large", "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": 1739484613.2267, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1739484611.0821, "finish": 1739484613.2267, "ip": "", "conv_id": "4dd9bbb3a38d4a928fe8a29bc5ee1c60", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe test was introduced by Turing in his 1950 paper \"Computing Machinery and Intelligence\" while working at the University of Manchester. It opens with the words: \"I propose to consider the question, 'Can machines think? Because \"thinking\" is difficult to define, Turing chooses to \"replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\" Turing describes the new form of the problem in terms of a three-person game called the \"imitation game\", in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: \"Are there imaginable digital computers which would do well in the imitation game?\" This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that \"machines can think\".\nSince Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a \"mind\", \"understanding\", or \"consciousness\", regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.\nHistory"]], "corpus": "wikipedia"}
+{"tstamp": 1739484664.5631, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1739484663.8316, "finish": 1739484664.5631, "ip": "", "conv_id": "88e5d6dfc7fa43449319a1bd9505c895", "model_name": "text-embedding-3-large", "prompt": "Find me a planet near α 23h 06m 29.368s and δ −05° 02′ 29.04″ that could be habitable.", "output": [["Find me a planet near α 23h 06m 29.368s and δ −05° 02′ 29.04″ that could be habitable.", "Alpha Centauri\n\nIn 2009, computer simulations showed that a planet might have been able to form near the inner edge of Alpha Centauri B's habitable zone, which extends from  from the star. Certain special assumptions, such as considering that the Alpha Centauri pair may have initially formed with a wider separation and later moved closer to each other (as might be possible if they formed in a dense star cluster), would permit an accretion-friendly environment farther from the star. Bodies around Alpha Centauri A would be able to orbit at slightly farther distances due to its stronger gravity. In addition, the lack of any brown dwarfs or gas giants in close orbits around Alpha Centauri make the likelihood of terrestrial planets greater than otherwise. A theoretical study indicates that a radial velocity analysis might detect a hypothetical planet of  in Alpha Centauri B's habitable zone.\nRadial velocity measurements of Alpha Centauri B made with the High Accuracy Radial Velocity Planet Searcher spectrograph were sufficiently sensitive to detect a  planet within the habitable zone of the star (i.e. with an orbital period P = 200 days), but no planets were detected.\nCurrent estimates place the probability of finding an Earth-like planet around Alpha Centauri at roughly 75%. The observational thresholds for planet detection in the habitable zones by the radial velocity method are currently (2017) estimated to be about  for Alpha Centauri A,  for Alpha Centauri B, and  for Proxima Centauri.\nEarly computer-generated models of planetary formation predicted the existence of terrestrial planets around both Alpha Centauri A and B, but most recent numerical investigations have shown that the gravitational pull of the companion star renders the accretion of planets difficult. Despite these difficulties, given the similarities to the Sun in spectral types, star type, age and probable stability of the orbits, it has been suggested that this stellar system could hold one of the best possibilities for harbouring extraterrestrial life on a potential planet."]], "corpus": "wikipedia"}
+{"tstamp": 1739484664.5631, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1739484663.8316, "finish": 1739484664.5631, "ip": "", "conv_id": "2f390022a6bc49a9b01fe99b15aa9cfc", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "Find me a planet near α 23h 06m 29.368s and δ −05° 02′ 29.04″ that could be habitable.", "output": [["Find me a planet near α 23h 06m 29.368s and δ −05° 02′ 29.04″ that could be habitable.", "List of nearest terrestrial exoplanet candidates\n\nOn August 24, 2016, astronomers announced the discovery of a rocky planet in the habitable zone of Proxima Centauri, the closest star to Earth (not counting the Sun). Called Proxima b, the planet is 1.3 times the mass of Earth and has an orbital period of roughly 11.2 Earth days. However, Proxima Centauri's classification as a red dwarf casts doubts on the habitability of any exoplanets in its orbit due to low stellar flux, high probability of tidal locking, small circumstellar habitable zones and high stellar variation. Another likely candidate is Alpha Centauri, Earth's nearest Sun-like star system 4.37 light-years away. Estimates place the probability of finding a habitable planet around Alpha Centauri A or B at roughly 75%. Alpha Centauri is the target of several exoplanet-finding missions, including Breakthrough Starshot and Mission Centaur, the latter of which is chronicled in the 2016 documentary film The Search for Earth Proxima.\nData Table\nNote: There is no scientific consensus about terrestrial composition of most of the planets in the list. Sources in the \"Main source\" column confirm the possibility of terrestrial composition.\nIn September 2012, the discovery of two planets orbiting Gliese 163 was announced.  One of the planets, Gliese 163 c, about 6.9 times the mass of Earth and somewhat hotter, was considered to be within the habitable zone, but is probably not terrestrial.\nIn May 2016, the finding of three Earth-like planets of ultracool dwarf TRAPPIST-1 has been released.\nThe existence of the planet Gliese 832 c was refuted in 2022, when a study found that the radial velocity signal shows characteristics of a signal originating from stellar activity, and not from a planet.\nStatistics\nNote: in most cases the composition of the atmosphere and atmosphere pressure of exoplanets are unknown, so surface temperatures are estimates based on computer models and expert opinions."]], "corpus": "wikipedia"}
+{"tstamp": 1739484750.4467, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1739484748.5363, "finish": 1739484750.4467, "ip": "", "conv_id": "54453e497b7443779bed3ebbaa00bae5", "model_name": "text-embedding-3-large", "prompt": "Paper explaining causal effects in neural network predictions using counterfactual examples.", "output": [["Paper explaining causal effects in neural network predictions using counterfactual examples.", "Title: Neural Causal Models for Counterfactual Identification and Estimation\n\nAbstract: Evaluating hypothetical statements about how the world would be had a different course of action been taken is arguably one key capability expected from modern AI systems. Counterfactual reasoning underpins discussions in fairness, the determination of blame and responsibility, credit assignment, and regret. In this paper, we study the evaluation of counterfactual statements through neural models. Specifically, we tackle two causal problems required to make such evaluations, i.e., counterfactual identification and estimation from an arbitrary combination of observational and experimental data. First, we show that neural causal models (NCMs) are expressive enough and encode the structural constraints necessary for performing counterfactual reasoning. Second, we develop an algorithm for simultaneously identifying and estimating counterfactual distributions. We show that this algorithm is sound and complete for deciding counterfactual identification in general settings. Third, considering the practical implications of these results, we introduce a new strategy for modeling NCMs using generative adversarial networks. Simulations corroborate with the proposed methodology."]], "corpus": "arxiv"}
+{"tstamp": 1739484750.4467, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1739484748.5363, "finish": 1739484750.4467, "ip": "", "conv_id": "c23d1fb938aa4c2fb40c0fb328f81877", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "Paper explaining causal effects in neural network predictions using counterfactual examples.", "output": [["Paper explaining causal effects in neural network predictions using counterfactual examples.", "Title: Towards Learning and Explaining Indirect Causal Effects in Neural Networks\n\nAbstract: Recently, there has been a growing interest in learning and explaining causal effects within Neural Network (NN) models. By virtue of NN architectures, previous approaches consider only direct and total causal effects assuming independence among input variables. We view an NN as a structural causal model (SCM) and extend our focus to include indirect causal effects by introducing feedforward connections among input neurons. We propose an ante-hoc method that captures and maintains direct, indirect, and total causal effects during NN model training. We also propose an algorithm for quantifying learned causal effects in an NN model and efficient approximation strategies for quantifying causal effects in high-dimensional data. Extensive experiments conducted on synthetic and real-world datasets demonstrate that the causal effects learned by our ante-hoc method better approximate the ground truth effects compared to existing methods."]], "corpus": "arxiv"}

data/retrieval_side_by_side-1b2a6102-80bb-417d-a081-cf780f49c66c.jsonl

@@ -32,3 +32,5 @@
 {"tstamp": 1739445488.8107, "task_type": "retrieval", "type": "tievote", "models": ["Alibaba-NLP/gte-Qwen2-7B-instruct", "BAAI/bge-large-en-v1.5"], "ip": "", "0_conv_id": "f573f23ad70c4e9b88030e35577366de", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": "Ram charan", "0_output": [["Ram charan", "Ram Charan\n\nKonidela Ram Charan (born 27 March 1985) is an Indian actor, producer, and entrepreneur who primarily works in Telugu cinema. He is one of the highest-paid actors in Indian cinema and has featured in Forbes Indias Celebrity 100 list since 2013. Charan is the recipient of several awards, including three Filmfare Awards and two Nandi Awards.\nCharan made his acting debut with the action film Chirutha (2007), a box office hit, winning the Filmfare Award for Best Male Debut – South. He rose to prominence starring in S. S. Rajamouli's fantasy action film Magadheera (2009), the highest-grossing Telugu film of all time at the time of its release, winning the Filmfare Award for Best Actor – Telugu. He went on to star in notable films such as Racha (2012), Naayak (2013), Yevadu (2014), Govindudu Andarivadele (2014), and Dhruva (2016)."]], "0_corpus": "wikipedia", "1_conv_id": "04da73f7289e4f0a985530ab2e269947", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "Ram charan", "1_output": [["Ram charan", "Ram Charan\n\nKonidela Ram Charan (born 27 March 1985) is an Indian actor, producer, and entrepreneur who primarily works in Telugu cinema. He is one of the highest-paid actors in Indian cinema and has featured in Forbes Indias Celebrity 100 list since 2013. Charan is the recipient of several awards, including three Filmfare Awards and two Nandi Awards.\nCharan made his acting debut with the action film Chirutha (2007), a box office hit, winning the Filmfare Award for Best Male Debut – South. He rose to prominence starring in S. S. Rajamouli's fantasy action film Magadheera (2009), the highest-grossing Telugu film of all time at the time of its release, winning the Filmfare Award for Best Actor – Telugu. He went on to star in notable films such as Racha (2012), Naayak (2013), Yevadu (2014), Govindudu Andarivadele (2014), and Dhruva (2016)."]], "1_corpus": "wikipedia"}
 {"tstamp": 1739479280.1121, "task_type": "retrieval", "type": "leftvote", "models": ["nomic-ai/nomic-embed-text-v1.5", "sentence-transformers/all-MiniLM-L6-v2"], "ip": "", "0_conv_id": "20750ffb152e43178c64e213fb8bdc76", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": "I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "0_output": [["I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "Title: MTEB: Massive Text Embedding Benchmark\n\nAbstract: Text embeddings are commonly evaluated on a small set of datasets from a single task not covering their possible applications to other tasks. It is unclear whether state-of-the-art embeddings on semantic textual similarity (STS) can be equally well applied to other tasks like clustering or reranking. This makes progress in the field difficult to track, as various models are constantly being proposed without proper evaluation. To solve this problem, we introduce the Massive Text Embedding Benchmark (MTEB). MTEB spans 8 embedding tasks covering a total of 58 datasets and 112 languages. Through the benchmarking of 33 models on MTEB, we establish the most comprehensive benchmark of text embeddings to date. We find that no particular text embedding method dominates across all tasks. This suggests that the field has yet to converge on a universal text embedding method and scale it up sufficiently to provide state-of-the-art results on all embedding tasks. MTEB comes with open-source code and a public leaderboard at https://github.com/embeddings-benchmark/mteb."]], "0_corpus": "arxiv", "1_conv_id": "1a000582c02640fcaf861a8a4720971e", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": "I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "1_output": [["I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "Title: Gauge fixing, BRS invariance and Ward identities for randomly stirred flows\n\nAbstract: The Galilean invariance of the Navier-Stokes equation is shown to be akin to a global gauge symmetry familiar from quantum field theory. This symmetry leads to a multiple counting of infinitely many inertial reference frames in the path integral approach to randomly stirred fluids. This problem is solved by fixing the gauge, i.e., singling out one reference frame. The gauge fixed theory has an underlying Becchi-Rouet-Stora (BRS) symmetry which leads to the Ward identity relating the exact inverse response and vertex functions. This identification of Galilean invariance as a gauge symmetry is explored in detail, for different gauge choices and by performing a rigorous examination of a discretized version of the theory. The Navier-Stokes equation is also invariant under arbitrary rectilinear frame accelerations, known as extended Galilean invariance (EGI). We gauge fix this extended symmetry and derive the generalized Ward identity that follows from the BRS invariance of the gauge-fixed theory. This new Ward identity reduces to the standard one in the limit of zero acceleration. This gauge-fixing approach unambiguously shows that Galilean invariance and EGI constrain only the zero mode of the vertex but none of the higher wavenumber modes."]], "1_corpus": "arxiv"}
 {"tstamp": 1739479303.8293, "task_type": "retrieval", "type": "leftvote", "models": ["nomic-ai/nomic-embed-text-v1.5", "sentence-transformers/all-MiniLM-L6-v2"], "ip": "", "0_conv_id": "6c4d2a5abda5414d8bd6b539b768efd5", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": "I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "0_output": [["I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "Title: MTEB: Massive Text Embedding Benchmark\n\nAbstract: Text embeddings are commonly evaluated on a small set of datasets from a single task not covering their possible applications to other tasks. It is unclear whether state-of-the-art embeddings on semantic textual similarity (STS) can be equally well applied to other tasks like clustering or reranking. This makes progress in the field difficult to track, as various models are constantly being proposed without proper evaluation. To solve this problem, we introduce the Massive Text Embedding Benchmark (MTEB). MTEB spans 8 embedding tasks covering a total of 58 datasets and 112 languages. Through the benchmarking of 33 models on MTEB, we establish the most comprehensive benchmark of text embeddings to date. We find that no particular text embedding method dominates across all tasks. This suggests that the field has yet to converge on a universal text embedding method and scale it up sufficiently to provide state-of-the-art results on all embedding tasks. MTEB comes with open-source code and a public leaderboard at https://github.com/embeddings-benchmark/mteb."]], "0_corpus": "arxiv", "1_conv_id": "b92926b48ebc4eab8d5aeaa9a4c81d15", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": "I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "1_output": [["I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "Title: Gauge fixing, BRS invariance and Ward identities for randomly stirred flows\n\nAbstract: The Galilean invariance of the Navier-Stokes equation is shown to be akin to a global gauge symmetry familiar from quantum field theory. This symmetry leads to a multiple counting of infinitely many inertial reference frames in the path integral approach to randomly stirred fluids. This problem is solved by fixing the gauge, i.e., singling out one reference frame. The gauge fixed theory has an underlying Becchi-Rouet-Stora (BRS) symmetry which leads to the Ward identity relating the exact inverse response and vertex functions. This identification of Galilean invariance as a gauge symmetry is explored in detail, for different gauge choices and by performing a rigorous examination of a discretized version of the theory. The Navier-Stokes equation is also invariant under arbitrary rectilinear frame accelerations, known as extended Galilean invariance (EGI). We gauge fix this extended symmetry and derive the generalized Ward identity that follows from the BRS invariance of the gauge-fixed theory. This new Ward identity reduces to the standard one in the limit of zero acceleration. This gauge-fixing approach unambiguously shows that Galilean invariance and EGI constrain only the zero mode of the vertex but none of the higher wavenumber modes."]], "1_corpus": "arxiv"}
+{"tstamp": 1739484647.6303, "task_type": "retrieval", "type": "tievote", "models": ["text-embedding-3-large", "intfloat/multilingual-e5-large-instruct"], "ip": "", "0_conv_id": "41e7a47d3cc44868966615c632bf2c3f", "0_model_name": "text-embedding-3-large", "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": "4dd9bbb3a38d4a928fe8a29bc5ee1c60", "1_model_name": "intfloat/multilingual-e5-large-instruct", "1_prompt": "Which test was devised to determine whether robots can think?", "1_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe test was introduced by Turing in his 1950 paper \"Computing Machinery and Intelligence\" while working at the University of Manchester. It opens with the words: \"I propose to consider the question, 'Can machines think? Because \"thinking\" is difficult to define, Turing chooses to \"replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\" Turing describes the new form of the problem in terms of a three-person game called the \"imitation game\", in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: \"Are there imaginable digital computers which would do well in the imitation game?\" This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that \"machines can think\".\nSince Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a \"mind\", \"understanding\", or \"consciousness\", regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.\nHistory"]], "1_corpus": "wikipedia"}
+{"tstamp": 1739484718.953, "task_type": "retrieval", "type": "leftvote", "models": ["text-embedding-3-large", "intfloat/multilingual-e5-large-instruct"], "ip": "", "0_conv_id": "88e5d6dfc7fa43449319a1bd9505c895", "0_model_name": "text-embedding-3-large", "0_prompt": "Find me a planet near α 23h 06m 29.368s and δ −05° 02′ 29.04″ that could be habitable.", "0_output": [["Find me a planet near α 23h 06m 29.368s and δ −05° 02′ 29.04″ that could be habitable.", "Alpha Centauri\n\nIn 2009, computer simulations showed that a planet might have been able to form near the inner edge of Alpha Centauri B's habitable zone, which extends from  from the star. Certain special assumptions, such as considering that the Alpha Centauri pair may have initially formed with a wider separation and later moved closer to each other (as might be possible if they formed in a dense star cluster), would permit an accretion-friendly environment farther from the star. Bodies around Alpha Centauri A would be able to orbit at slightly farther distances due to its stronger gravity. In addition, the lack of any brown dwarfs or gas giants in close orbits around Alpha Centauri make the likelihood of terrestrial planets greater than otherwise. A theoretical study indicates that a radial velocity analysis might detect a hypothetical planet of  in Alpha Centauri B's habitable zone.\nRadial velocity measurements of Alpha Centauri B made with the High Accuracy Radial Velocity Planet Searcher spectrograph were sufficiently sensitive to detect a  planet within the habitable zone of the star (i.e. with an orbital period P = 200 days), but no planets were detected.\nCurrent estimates place the probability of finding an Earth-like planet around Alpha Centauri at roughly 75%. The observational thresholds for planet detection in the habitable zones by the radial velocity method are currently (2017) estimated to be about  for Alpha Centauri A,  for Alpha Centauri B, and  for Proxima Centauri.\nEarly computer-generated models of planetary formation predicted the existence of terrestrial planets around both Alpha Centauri A and B, but most recent numerical investigations have shown that the gravitational pull of the companion star renders the accretion of planets difficult. Despite these difficulties, given the similarities to the Sun in spectral types, star type, age and probable stability of the orbits, it has been suggested that this stellar system could hold one of the best possibilities for harbouring extraterrestrial life on a potential planet."]], "0_corpus": "wikipedia", "1_conv_id": "2f390022a6bc49a9b01fe99b15aa9cfc", "1_model_name": "intfloat/multilingual-e5-large-instruct", "1_prompt": "Find me a planet near α 23h 06m 29.368s and δ −05° 02′ 29.04″ that could be habitable.", "1_output": [["Find me a planet near α 23h 06m 29.368s and δ −05° 02′ 29.04″ that could be habitable.", "List of nearest terrestrial exoplanet candidates\n\nOn August 24, 2016, astronomers announced the discovery of a rocky planet in the habitable zone of Proxima Centauri, the closest star to Earth (not counting the Sun). Called Proxima b, the planet is 1.3 times the mass of Earth and has an orbital period of roughly 11.2 Earth days. However, Proxima Centauri's classification as a red dwarf casts doubts on the habitability of any exoplanets in its orbit due to low stellar flux, high probability of tidal locking, small circumstellar habitable zones and high stellar variation. Another likely candidate is Alpha Centauri, Earth's nearest Sun-like star system 4.37 light-years away. Estimates place the probability of finding a habitable planet around Alpha Centauri A or B at roughly 75%. Alpha Centauri is the target of several exoplanet-finding missions, including Breakthrough Starshot and Mission Centaur, the latter of which is chronicled in the 2016 documentary film The Search for Earth Proxima.\nData Table\nNote: There is no scientific consensus about terrestrial composition of most of the planets in the list. Sources in the \"Main source\" column confirm the possibility of terrestrial composition.\nIn September 2012, the discovery of two planets orbiting Gliese 163 was announced.  One of the planets, Gliese 163 c, about 6.9 times the mass of Earth and somewhat hotter, was considered to be within the habitable zone, but is probably not terrestrial.\nIn May 2016, the finding of three Earth-like planets of ultracool dwarf TRAPPIST-1 has been released.\nThe existence of the planet Gliese 832 c was refuted in 2022, when a study found that the radial velocity signal shows characteristics of a signal originating from stellar activity, and not from a planet.\nStatistics\nNote: in most cases the composition of the atmosphere and atmosphere pressure of exoplanets are unknown, so surface temperatures are estimates based on computer models and expert opinions."]], "1_corpus": "wikipedia"}