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{"tstamp": 1725744583.6767, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "3174aad863794924a66790c142f8249d", "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": "1855cb586e1c48c6932c2f2d885db6a0", "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"}
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{"tstamp": 1725777386.1694, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "204ba524f02b454cb31f284cadb61272", "0_model_name": "embed-english-v3.0", "0_prompt": "Enhancing speaker representation in self-supervised learning with new data augmentation and contrastive loss methods", "0_output": [["Enhancing speaker representation in self-supervised learning with new data augmentation and contrastive loss methods", "Title: A comprehensive study on self-supervised distillation for speaker representation learning\n\nAbstract: In real application scenarios, it is often challenging to obtain a large amount of labeled data for speaker representation learning due to speaker privacy concerns. Self-supervised learning with no labels has become a more and more promising way to solve it. Compared with contrastive learning, self-distilled approaches use only positive samples in the loss function and thus are more attractive. In this paper, we present a comprehensive study on self-distilled self-supervised speaker representation learning, especially on critical data augmentation. Our proposed strategy of audio perturbation augmentation has pushed the performance of the speaker representation to a new limit. The experimental results show that our model can achieve a new SoTA on Voxceleb1 speaker verification evaluation benchmark ( i.e., equal error rate (EER) 2.505%, 2.473%, and 4.791% for trial Vox1-O, Vox1-E and Vox1-H , respectively), discarding any speaker labels in the training phase."]], "0_corpus": "arxiv", "1_conv_id": "aa824b4fb0614c6181fba27461922d38", "1_model_name": "voyage-multilingual-2", "1_prompt": "Enhancing speaker representation in self-supervised learning with new data augmentation and contrastive loss methods", "1_output": [["Enhancing speaker representation in self-supervised learning with new data augmentation and contrastive loss methods", "Title: Augmentation adversarial training for self-supervised speaker recognition\n\nAbstract: The goal of this work is to train robust speaker recognition models without speaker labels. Recent works on unsupervised speaker representations are based on contrastive learning in which they encourage within-utterance embeddings to be similar and across-utterance embeddings to be dissimilar. However, since the within-utterance segments share the same acoustic characteristics, it is difficult to separate the speaker information from the channel information. To this end, we propose augmentation adversarial training strategy that trains the network to be discriminative for the speaker information, while invariant to the augmentation applied. Since the augmentation simulates the acoustic characteristics, training the network to be invariant to augmentation also encourages the network to be invariant to the channel information in general. Extensive experiments on the VoxCeleb and VOiCES datasets show significant improvements over previous works using self-supervision, and the performance of our self-supervised models far exceed that of humans."]], "1_corpus": "arxiv"}
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{"tstamp": 1725797674.1714, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "c9f3be2e803740658137e95bcea700d8", "0_model_name": "text-embedding-004", "0_prompt": "I am looking for a paper to help me deal with data constraints as I scale up my language model.", "0_output": [["I am looking for a paper to help me deal with data constraints as I scale up my language model.", "Title: Scaling Data-Constrained Language Models\n\nAbstract: The current trend of scaling language models involves increasing both parameter count and training dataset size. Extrapolating this trend suggests that training dataset size may soon be limited by the amount of text data available on the internet. Motivated by this limit, we investigate scaling language models in data-constrained regimes. Specifically, we run a large set of experiments varying the extent of data repetition and compute budget, ranging up to 900 billion training tokens and 9 billion parameter models. We find that with constrained data for a fixed compute budget, training with up to 4 epochs of repeated data yields negligible changes to loss compared to having unique data. However, with more repetition, the value of adding compute eventually decays to zero. We propose and empirically validate a scaling law for compute optimality that accounts for the decreasing value of repeated tokens and excess parameters. Finally, we experiment with approaches mitigating data scarcity, including augmenting the training dataset with code data or removing commonly used filters. Models and datasets from our 400 training runs are freely available at https://github.com/huggingface/datablations."]], "0_corpus": "arxiv", "1_conv_id": "9a495861aea549b09b93c2055ded0093", "1_model_name": "BM25", "1_prompt": "I am looking for a paper to help me deal with data constraints as I scale up my language model.", "1_output": [["I am looking for a paper to help me deal with data constraints as I scale up my language model.", "Title: Michael E. Fisher - teacher, mentor, colleague and friend: a (very) personal account\n\nAbstract: The only rational way of educating is to be an example. If one cant help it, a warning example. Albert Einstein. I had the good fortune and privilege of having Michael Fisher as my teacher, supervisor, mentor and friend. During my years as a scientist, teacher and supervisor of about one hundred students and post docs I found myself innumerable times realizing that I am following or at least trying to follow Michaels example. These pages are my attempt to convey recollections of my association with Michael, focusing on how he served as an example for me."]], "1_corpus": "arxiv"}
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{"tstamp": 1725744583.6767, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "3174aad863794924a66790c142f8249d", "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": "1855cb586e1c48c6932c2f2d885db6a0", "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"}
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{"tstamp": 1725777386.1694, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "204ba524f02b454cb31f284cadb61272", "0_model_name": "embed-english-v3.0", "0_prompt": "Enhancing speaker representation in self-supervised learning with new data augmentation and contrastive loss methods", "0_output": [["Enhancing speaker representation in self-supervised learning with new data augmentation and contrastive loss methods", "Title: A comprehensive study on self-supervised distillation for speaker representation learning\n\nAbstract: In real application scenarios, it is often challenging to obtain a large amount of labeled data for speaker representation learning due to speaker privacy concerns. Self-supervised learning with no labels has become a more and more promising way to solve it. Compared with contrastive learning, self-distilled approaches use only positive samples in the loss function and thus are more attractive. In this paper, we present a comprehensive study on self-distilled self-supervised speaker representation learning, especially on critical data augmentation. Our proposed strategy of audio perturbation augmentation has pushed the performance of the speaker representation to a new limit. The experimental results show that our model can achieve a new SoTA on Voxceleb1 speaker verification evaluation benchmark ( i.e., equal error rate (EER) 2.505%, 2.473%, and 4.791% for trial Vox1-O, Vox1-E and Vox1-H , respectively), discarding any speaker labels in the training phase."]], "0_corpus": "arxiv", "1_conv_id": "aa824b4fb0614c6181fba27461922d38", "1_model_name": "voyage-multilingual-2", "1_prompt": "Enhancing speaker representation in self-supervised learning with new data augmentation and contrastive loss methods", "1_output": [["Enhancing speaker representation in self-supervised learning with new data augmentation and contrastive loss methods", "Title: Augmentation adversarial training for self-supervised speaker recognition\n\nAbstract: The goal of this work is to train robust speaker recognition models without speaker labels. Recent works on unsupervised speaker representations are based on contrastive learning in which they encourage within-utterance embeddings to be similar and across-utterance embeddings to be dissimilar. However, since the within-utterance segments share the same acoustic characteristics, it is difficult to separate the speaker information from the channel information. To this end, we propose augmentation adversarial training strategy that trains the network to be discriminative for the speaker information, while invariant to the augmentation applied. Since the augmentation simulates the acoustic characteristics, training the network to be invariant to augmentation also encourages the network to be invariant to the channel information in general. Extensive experiments on the VoxCeleb and VOiCES datasets show significant improvements over previous works using self-supervision, and the performance of our self-supervised models far exceed that of humans."]], "1_corpus": "arxiv"}
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{"tstamp": 1725797674.1714, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "c9f3be2e803740658137e95bcea700d8", "0_model_name": "text-embedding-004", "0_prompt": "I am looking for a paper to help me deal with data constraints as I scale up my language model.", "0_output": [["I am looking for a paper to help me deal with data constraints as I scale up my language model.", "Title: Scaling Data-Constrained Language Models\n\nAbstract: The current trend of scaling language models involves increasing both parameter count and training dataset size. Extrapolating this trend suggests that training dataset size may soon be limited by the amount of text data available on the internet. Motivated by this limit, we investigate scaling language models in data-constrained regimes. Specifically, we run a large set of experiments varying the extent of data repetition and compute budget, ranging up to 900 billion training tokens and 9 billion parameter models. We find that with constrained data for a fixed compute budget, training with up to 4 epochs of repeated data yields negligible changes to loss compared to having unique data. However, with more repetition, the value of adding compute eventually decays to zero. We propose and empirically validate a scaling law for compute optimality that accounts for the decreasing value of repeated tokens and excess parameters. Finally, we experiment with approaches mitigating data scarcity, including augmenting the training dataset with code data or removing commonly used filters. Models and datasets from our 400 training runs are freely available at https://github.com/huggingface/datablations."]], "0_corpus": "arxiv", "1_conv_id": "9a495861aea549b09b93c2055ded0093", "1_model_name": "BM25", "1_prompt": "I am looking for a paper to help me deal with data constraints as I scale up my language model.", "1_output": [["I am looking for a paper to help me deal with data constraints as I scale up my language model.", "Title: Michael E. Fisher - teacher, mentor, colleague and friend: a (very) personal account\n\nAbstract: The only rational way of educating is to be an example. If one cant help it, a warning example. Albert Einstein. I had the good fortune and privilege of having Michael Fisher as my teacher, supervisor, mentor and friend. During my years as a scientist, teacher and supervisor of about one hundred students and post docs I found myself innumerable times realizing that I am following or at least trying to follow Michaels example. These pages are my attempt to convey recollections of my association with Michael, focusing on how he served as an example for me."]], "1_corpus": "arxiv"}
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{"tstamp": 1725803525.8199, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "3254112b3c444274a8855d65ec8aa11d", "0_model_name": "BM25", "0_prompt": "what is the main source of nutrients in an estuary", "0_output": [["what is the main source of nutrients in an estuary", "Columbia River\n\nNutrient cycle Both natural and anthropogenic processes are involved in the cycling of nutrients in the Columbia River basin. Natural processes in the system include estuarine mixing of fresh and ocean waters, and climate variability patterns such as the Pacific Decadal Oscillation and the El Nino Southern Oscillation (both climatic cycles that affect the amount of regional snowpack and river discharge). Natural sources of nutrients in the Columbia River include weathering, leaf litter, salmon carcasses, runoff from its tributaries, and ocean estuary exchange. Major anthropogenic impacts on nutrients in the basin are due to fertilizers from agriculture, sewage systems, logging, and the construction of dams.\nNutrient dynamics vary in the river basin from the headwaters to the main river and dams, to finally reaching the Columbia River estuary and ocean. Upstream in the headwaters, salmon runs are the main source of nutrients. Dams along the river impact nutrient cycling by increasing residence time of nutrients, and reducing the transport of silicate to the estuary, which directly impacts diatoms, a type of phytoplankton. The dams are also a barrier to salmon migration and can increase the amount of methane locally produced. The Columbia River estuary exports high rates of nutrients into the Pacific, except for nitrogen, which is delivered into the estuary by ocean upwelling sources."]], "0_corpus": "wikipedia", "1_conv_id": "0ab8eba13bf34658b60d99f89210bd61", "1_model_name": "GritLM/GritLM-7B", "1_prompt": "what is the main source of nutrients in an estuary", "1_output": [["what is the main source of nutrients in an estuary", "Columbia River\n\nNutrient cycle Both natural and anthropogenic processes are involved in the cycling of nutrients in the Columbia River basin. Natural processes in the system include estuarine mixing of fresh and ocean waters, and climate variability patterns such as the Pacific Decadal Oscillation and the El Nino Southern Oscillation (both climatic cycles that affect the amount of regional snowpack and river discharge). Natural sources of nutrients in the Columbia River include weathering, leaf litter, salmon carcasses, runoff from its tributaries, and ocean estuary exchange. Major anthropogenic impacts on nutrients in the basin are due to fertilizers from agriculture, sewage systems, logging, and the construction of dams.\nNutrient dynamics vary in the river basin from the headwaters to the main river and dams, to finally reaching the Columbia River estuary and ocean. Upstream in the headwaters, salmon runs are the main source of nutrients. Dams along the river impact nutrient cycling by increasing residence time of nutrients, and reducing the transport of silicate to the estuary, which directly impacts diatoms, a type of phytoplankton. The dams are also a barrier to salmon migration and can increase the amount of methane locally produced. The Columbia River estuary exports high rates of nutrients into the Pacific, except for nitrogen, which is delivered into the estuary by ocean upwelling sources."]], "1_corpus": "wikipedia"}
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{"tstamp": 1725803560.6556, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "05cf2ac7ab2245a1b59a99af01719f53", "0_model_name": "Salesforce/SFR-Embedding-2_R", "0_prompt": "What is the specification of itel p65 phone", "0_output": [["What is the specification of itel p65 phone", "Itel Mobile\n\nitel Mobile is a Chinese mobile phone manufacturer company that was founded by Transsion Holdings Co Limited in March 2008.\nTheir products are mainly sold in various emerging markets, including parts of Africa, South Asia, Southeast Asia, Europe and Latin America."]], "0_corpus": "wikipedia", "1_conv_id": "4d66db400b214f07be043702c5ad4092", "1_model_name": "GritLM/GritLM-7B", "1_prompt": "What is the specification of itel p65 phone", "1_output": [["What is the specification of itel p65 phone", "Itel Mobile\n\nitel Mobile is a Chinese mobile phone manufacturer company that was founded by Transsion Holdings Co Limited in March 2008.\nTheir products are mainly sold in various emerging markets, including parts of Africa, South Asia, Southeast Asia, Europe and Latin America."]], "1_corpus": "wikipedia"}
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{"tstamp": 1725803595.1943, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "26b969ece26f450584d9d24e2114d438", "0_model_name": "Salesforce/SFR-Embedding-2_R", "0_prompt": "What is shilajin", "0_output": [["What is shilajin", "Shilajit\n\nShilajit (; , 'conqueror of the rocks'), salajeet (), mumijo or mumlayi or mumie is an organic-mineral product of predominantly biological origin, formed in the mountains (in mountain crevices and caves).\nA blackish-brown powder or an exudate from high mountain rocks, often found in the Himalayas, the Pamir Mountains, Afghanistan, (primarily in Gorno-Badakhshan, Tajikistan), (Karakoram, Gilgit-Baltistan in Pakistan, Nepal, Bhutan, the Caucasus Mountains within Russia, Altai, Central Asia, Iran, Mongolia and in southern Peru, where it is called Andean shilajit. The peoples of the East used shilajit in folk and non-traditional (alternative) medicine (Ayurveda, Chinese, Tibetan). Shilajit is sold both in dry extract form and in dietary supplements.\nHistory\nSince ancient times, shilajit has been a folk medicine in Afghanistan, India, Iran, China, Pakistan, Nepal, Central Asia and Tibet. Shilajit has been used as a folk medicine and in alternative medicine for more than four thousand years. The healing effects of shilajit for different diseases is mentioned in the works of Aristotle, Razi, Biruni, Ibn Sina and others.\nD'Herbelot, in his 1821 publication, stated that the Persians used the substance called mumiay, or mummy, as a potent cure-all to address broken bones and disease."]], "0_corpus": "wikipedia", "1_conv_id": "d2dc3833b2e546949f3ab8622f72d693", "1_model_name": "text-embedding-004", "1_prompt": "What is shilajin", "1_output": [["What is shilajin", "Shinjū\n\nis a Japanese term meaning \"double suicide\", used in common parlance to refer to any group suicide of two or more individuals bound by love, typically lovers, parents and children, and even whole families. A double suicide without consent is called and it is considered as a sort of murder–suicide.\nLovers committing double suicide believed that they would be united again in heaven, a view supported by feudal teaching in Edo period Japan, which taught that the bond between two lovers is continued into the next world, and by the teaching of Pure Land Buddhism wherein it is believed that through double suicide, one can approach rebirth in the Pure Land."]], "1_corpus": "wikipedia"}
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{"tstamp": 1725803643.8096, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "e3a925c1db7345798195cff86e92db6c", "0_model_name": "voyage-multilingual-2", "0_prompt": "What is the most powerful explosion in the universe ", "0_output": [["What is the most powerful explosion in the universe ", "Explosion\n\nAstronomical Among the largest known explosions in the universe are supernovae, which occur after the end of life of some types of stars. Solar flares are an example of common, much less energetic, explosions on the Sun, and presumably on most other stars as well. The energy source for solar flare activity comes from the tangling of magnetic field lines resulting from the rotation of the Sun's conductive plasma. Another type of large astronomical explosion occurs when a meteoroid or an asteroid impacts the surface of another object, or explodes in its atmosphere, such as a planet. This occurs because the two objects are moving at very high speed relative to each other (a minimum of for an Earth impacting body). For example, the Tunguska event of 1908 is believed to have resulted from a meteor air burst.\nBlack hole mergers, likely involving binary black hole systems, are capable of radiating many solar masses of energy into the universe in a fraction of a second, in the form of a gravitational wave. This is capable of transmitting ordinary energy and destructive forces to nearby objects, but in the vastness of space, nearby objects are rare. The gravitational wave observed on 21 May 2019, known as GW190521, produced a merger signal of about 100 ms duration, during which time is it estimated to have radiated away nine solar masses in the form of gravitational energy.\nChemical\nThe most common artificial explosives are chemical explosives, usually involving a rapid and violent oxidation reaction that produces large amounts of hot gas. Gunpowder was the first explosive to be invented and put to use. Other notable early developments in chemical explosive technology were Frederick Augustus Abel's development of nitrocellulose in 1865 and Alfred Nobel's invention of dynamite in 1866. Chemical explosions (both intentional and accidental) are often initiated by an electric spark or flame in the presence of oxygen. Accidental explosions may occur in fuel tanks, rocket engines, etc."]], "0_corpus": "wikipedia", "1_conv_id": "f287126ac2024db18f89fd75ffe13132", "1_model_name": "intfloat/multilingual-e5-large-instruct", "1_prompt": "What is the most powerful explosion in the universe ", "1_output": [["What is the most powerful explosion in the universe ", "Explosion\n\nAstronomical Among the largest known explosions in the universe are supernovae, which occur after the end of life of some types of stars. Solar flares are an example of common, much less energetic, explosions on the Sun, and presumably on most other stars as well. The energy source for solar flare activity comes from the tangling of magnetic field lines resulting from the rotation of the Sun's conductive plasma. Another type of large astronomical explosion occurs when a meteoroid or an asteroid impacts the surface of another object, or explodes in its atmosphere, such as a planet. This occurs because the two objects are moving at very high speed relative to each other (a minimum of for an Earth impacting body). For example, the Tunguska event of 1908 is believed to have resulted from a meteor air burst.\nBlack hole mergers, likely involving binary black hole systems, are capable of radiating many solar masses of energy into the universe in a fraction of a second, in the form of a gravitational wave. This is capable of transmitting ordinary energy and destructive forces to nearby objects, but in the vastness of space, nearby objects are rare. The gravitational wave observed on 21 May 2019, known as GW190521, produced a merger signal of about 100 ms duration, during which time is it estimated to have radiated away nine solar masses in the form of gravitational energy.\nChemical\nThe most common artificial explosives are chemical explosives, usually involving a rapid and violent oxidation reaction that produces large amounts of hot gas. Gunpowder was the first explosive to be invented and put to use. Other notable early developments in chemical explosive technology were Frederick Augustus Abel's development of nitrocellulose in 1865 and Alfred Nobel's invention of dynamite in 1866. Chemical explosions (both intentional and accidental) are often initiated by an electric spark or flame in the presence of oxygen. Accidental explosions may occur in fuel tanks, rocket engines, etc."]], "1_corpus": "wikipedia"}
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{"tstamp": 1725803703.8763, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "42be1d4e98cc4c8e8a82746d9b799d05", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "Give me the specification of Samsung galaxy S24 ultra ", "0_output": [["Give me the specification of Samsung galaxy S24 ultra ", "Samsung Galaxy S24\n\nCamera The Galaxy S24 and S24+ have a 50 MP wide sensor, a 10 MP 3x tele telephoto sensor and a 12 MP ultrawide sensor. The S24 Ultra has a 200 MP wide sensor, 50 MP 5× tele periscope telephoto sensor, 10 MP 3x tele telephoto sensor, and a 12 MP ultrawide sensor. The front camera uses a 12 MP sensor on all three models.\nBatteries\nThe Galaxy S24, S24+, and S24 Ultra contain non-removable 4,000 mAh, 4,900 mAh, and 5,000 mAh Li-ion batteries respectively. The S24 only charges at 25 watts, while the S24+ and S24 Ultra charge up at 45 watts charging.\nConnectivity\nThe Galaxy S24, and S24+ support 5G SA/NSA/Sub6, Wi-Fi 6E, and Bluetooth 5.3 connectivity, while the Galaxy S24 Ultra additionally supports Wi-Fi 7 and ultra-wideband.\nMemory and storage\nThe Galaxy S24 phones feature 4,800 MT/s LPDDR5X memory and Universal Flash Storage 3.1 with 128 GB or version 4.0 with 256 GB and above\nSoftware\nThe Samsung Galaxy S24 phones were released with Android 14 with Samsung's One UI 6.1 user experience. Samsung has promised 7 years of security patches and OS upgrades similar to that of Apple's.\nThey use Google's on-device Gemini Nano, already used by their own Pixel 8 Pro.\nReception\nHot S Pen\nSome users noted that the Galaxy S24 Ultra's S Pen \"absolutely reeks\", with a smell that was likened to \"burnt plastic\". A moderator on Samsung's EU forums explained,"]], "0_corpus": "wikipedia", "1_conv_id": "d68e7726dc8e42ef857f4058f12be152", "1_model_name": "BM25", "1_prompt": "Give me the specification of Samsung galaxy S24 ultra ", "1_output": [["Give me the specification of Samsung galaxy S24 ultra ", "Samsung Galaxy S24\n\nCamera The Galaxy S24 and S24+ have a 50 MP wide sensor, a 10 MP 3x tele telephoto sensor and a 12 MP ultrawide sensor. The S24 Ultra has a 200 MP wide sensor, 50 MP 5× tele periscope telephoto sensor, 10 MP 3x tele telephoto sensor, and a 12 MP ultrawide sensor. The front camera uses a 12 MP sensor on all three models.\nBatteries\nThe Galaxy S24, S24+, and S24 Ultra contain non-removable 4,000 mAh, 4,900 mAh, and 5,000 mAh Li-ion batteries respectively. The S24 only charges at 25 watts, while the S24+ and S24 Ultra charge up at 45 watts charging.\nConnectivity\nThe Galaxy S24, and S24+ support 5G SA/NSA/Sub6, Wi-Fi 6E, and Bluetooth 5.3 connectivity, while the Galaxy S24 Ultra additionally supports Wi-Fi 7 and ultra-wideband.\nMemory and storage\nThe Galaxy S24 phones feature 4,800 MT/s LPDDR5X memory and Universal Flash Storage 3.1 with 128 GB or version 4.0 with 256 GB and above\nSoftware\nThe Samsung Galaxy S24 phones were released with Android 14 with Samsung's One UI 6.1 user experience. Samsung has promised 7 years of security patches and OS upgrades similar to that of Apple's.\nThey use Google's on-device Gemini Nano, already used by their own Pixel 8 Pro.\nReception\nHot S Pen\nSome users noted that the Galaxy S24 Ultra's S Pen \"absolutely reeks\", with a smell that was likened to \"burnt plastic\". A moderator on Samsung's EU forums explained,"]], "1_corpus": "wikipedia"}
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{"tstamp": 1725777330.9505, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1725777330.5675, "finish": 1725777330.9505, "ip": "", "conv_id": "aa824b4fb0614c6181fba27461922d38", "model_name": "voyage-multilingual-2", "prompt": "Enhancing speaker representation in self-supervised learning with new data augmentation and contrastive loss methods", "output": [["Enhancing speaker representation in self-supervised learning with new data augmentation and contrastive loss methods", "Title: Augmentation adversarial training for self-supervised speaker recognition\n\nAbstract: The goal of this work is to train robust speaker recognition models without speaker labels. Recent works on unsupervised speaker representations are based on contrastive learning in which they encourage within-utterance embeddings to be similar and across-utterance embeddings to be dissimilar. However, since the within-utterance segments share the same acoustic characteristics, it is difficult to separate the speaker information from the channel information. To this end, we propose augmentation adversarial training strategy that trains the network to be discriminative for the speaker information, while invariant to the augmentation applied. Since the augmentation simulates the acoustic characteristics, training the network to be invariant to augmentation also encourages the network to be invariant to the channel information in general. Extensive experiments on the VoxCeleb and VOiCES datasets show significant improvements over previous works using self-supervision, and the performance of our self-supervised models far exceed that of humans."]], "corpus": "arxiv"}
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{"tstamp": 1725797632.549, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1725797630.6297, "finish": 1725797632.549, "ip": "", "conv_id": "c9f3be2e803740658137e95bcea700d8", "model_name": "text-embedding-004", "prompt": "I am looking for a paper to help me deal with data constraints as I scale up my language model.", "output": [["I am looking for a paper to help me deal with data constraints as I scale up my language model.", "Title: Scaling Data-Constrained Language Models\n\nAbstract: The current trend of scaling language models involves increasing both parameter count and training dataset size. Extrapolating this trend suggests that training dataset size may soon be limited by the amount of text data available on the internet. Motivated by this limit, we investigate scaling language models in data-constrained regimes. Specifically, we run a large set of experiments varying the extent of data repetition and compute budget, ranging up to 900 billion training tokens and 9 billion parameter models. We find that with constrained data for a fixed compute budget, training with up to 4 epochs of repeated data yields negligible changes to loss compared to having unique data. However, with more repetition, the value of adding compute eventually decays to zero. We propose and empirically validate a scaling law for compute optimality that accounts for the decreasing value of repeated tokens and excess parameters. Finally, we experiment with approaches mitigating data scarcity, including augmenting the training dataset with code data or removing commonly used filters. Models and datasets from our 400 training runs are freely available at https://github.com/huggingface/datablations."]], "corpus": "arxiv"}
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{"tstamp": 1725797632.549, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1725797630.6297, "finish": 1725797632.549, "ip": "", "conv_id": "9a495861aea549b09b93c2055ded0093", "model_name": "BM25", "prompt": "I am looking for a paper to help me deal with data constraints as I scale up my language model.", "output": [["I am looking for a paper to help me deal with data constraints as I scale up my language model.", "Title: Michael E. Fisher - teacher, mentor, colleague and friend: a (very) personal account\n\nAbstract: The only rational way of educating is to be an example. If one cant help it, a warning example. Albert Einstein. I had the good fortune and privilege of having Michael Fisher as my teacher, supervisor, mentor and friend. During my years as a scientist, teacher and supervisor of about one hundred students and post docs I found myself innumerable times realizing that I am following or at least trying to follow Michaels example. These pages are my attempt to convey recollections of my association with Michael, focusing on how he served as an example for me."]], "corpus": "arxiv"}
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{"tstamp": 1725777330.9505, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1725777330.5675, "finish": 1725777330.9505, "ip": "", "conv_id": "aa824b4fb0614c6181fba27461922d38", "model_name": "voyage-multilingual-2", "prompt": "Enhancing speaker representation in self-supervised learning with new data augmentation and contrastive loss methods", "output": [["Enhancing speaker representation in self-supervised learning with new data augmentation and contrastive loss methods", "Title: Augmentation adversarial training for self-supervised speaker recognition\n\nAbstract: The goal of this work is to train robust speaker recognition models without speaker labels. Recent works on unsupervised speaker representations are based on contrastive learning in which they encourage within-utterance embeddings to be similar and across-utterance embeddings to be dissimilar. However, since the within-utterance segments share the same acoustic characteristics, it is difficult to separate the speaker information from the channel information. To this end, we propose augmentation adversarial training strategy that trains the network to be discriminative for the speaker information, while invariant to the augmentation applied. Since the augmentation simulates the acoustic characteristics, training the network to be invariant to augmentation also encourages the network to be invariant to the channel information in general. Extensive experiments on the VoxCeleb and VOiCES datasets show significant improvements over previous works using self-supervision, and the performance of our self-supervised models far exceed that of humans."]], "corpus": "arxiv"}
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{"tstamp": 1725797632.549, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1725797630.6297, "finish": 1725797632.549, "ip": "", "conv_id": "c9f3be2e803740658137e95bcea700d8", "model_name": "text-embedding-004", "prompt": "I am looking for a paper to help me deal with data constraints as I scale up my language model.", "output": [["I am looking for a paper to help me deal with data constraints as I scale up my language model.", "Title: Scaling Data-Constrained Language Models\n\nAbstract: The current trend of scaling language models involves increasing both parameter count and training dataset size. Extrapolating this trend suggests that training dataset size may soon be limited by the amount of text data available on the internet. Motivated by this limit, we investigate scaling language models in data-constrained regimes. Specifically, we run a large set of experiments varying the extent of data repetition and compute budget, ranging up to 900 billion training tokens and 9 billion parameter models. We find that with constrained data for a fixed compute budget, training with up to 4 epochs of repeated data yields negligible changes to loss compared to having unique data. However, with more repetition, the value of adding compute eventually decays to zero. We propose and empirically validate a scaling law for compute optimality that accounts for the decreasing value of repeated tokens and excess parameters. Finally, we experiment with approaches mitigating data scarcity, including augmenting the training dataset with code data or removing commonly used filters. Models and datasets from our 400 training runs are freely available at https://github.com/huggingface/datablations."]], "corpus": "arxiv"}
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{"tstamp": 1725797632.549, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1725797630.6297, "finish": 1725797632.549, "ip": "", "conv_id": "9a495861aea549b09b93c2055ded0093", "model_name": "BM25", "prompt": "I am looking for a paper to help me deal with data constraints as I scale up my language model.", "output": [["I am looking for a paper to help me deal with data constraints as I scale up my language model.", "Title: Michael E. Fisher - teacher, mentor, colleague and friend: a (very) personal account\n\nAbstract: The only rational way of educating is to be an example. If one cant help it, a warning example. Albert Einstein. I had the good fortune and privilege of having Michael Fisher as my teacher, supervisor, mentor and friend. During my years as a scientist, teacher and supervisor of about one hundred students and post docs I found myself innumerable times realizing that I am following or at least trying to follow Michaels example. These pages are my attempt to convey recollections of my association with Michael, focusing on how he served as an example for me."]], "corpus": "arxiv"}
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{"tstamp": 1725803515.4065, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1725803512.4618, "finish": 1725803515.4065, "ip": "", "conv_id": "3254112b3c444274a8855d65ec8aa11d", "model_name": "BM25", "prompt": "what is the main source of nutrients in an estuary", "output": [["what is the main source of nutrients in an estuary", "Columbia River\n\nNutrient cycle Both natural and anthropogenic processes are involved in the cycling of nutrients in the Columbia River basin. Natural processes in the system include estuarine mixing of fresh and ocean waters, and climate variability patterns such as the Pacific Decadal Oscillation and the El Nino Southern Oscillation (both climatic cycles that affect the amount of regional snowpack and river discharge). Natural sources of nutrients in the Columbia River include weathering, leaf litter, salmon carcasses, runoff from its tributaries, and ocean estuary exchange. Major anthropogenic impacts on nutrients in the basin are due to fertilizers from agriculture, sewage systems, logging, and the construction of dams.\nNutrient dynamics vary in the river basin from the headwaters to the main river and dams, to finally reaching the Columbia River estuary and ocean. Upstream in the headwaters, salmon runs are the main source of nutrients. Dams along the river impact nutrient cycling by increasing residence time of nutrients, and reducing the transport of silicate to the estuary, which directly impacts diatoms, a type of phytoplankton. The dams are also a barrier to salmon migration and can increase the amount of methane locally produced. The Columbia River estuary exports high rates of nutrients into the Pacific, except for nitrogen, which is delivered into the estuary by ocean upwelling sources."]], "corpus": "wikipedia"}
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{"tstamp": 1725803515.4065, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1725803512.4618, "finish": 1725803515.4065, "ip": "", "conv_id": "0ab8eba13bf34658b60d99f89210bd61", "model_name": "GritLM/GritLM-7B", "prompt": "what is the main source of nutrients in an estuary", "output": [["what is the main source of nutrients in an estuary", "Columbia River\n\nNutrient cycle Both natural and anthropogenic processes are involved in the cycling of nutrients in the Columbia River basin. Natural processes in the system include estuarine mixing of fresh and ocean waters, and climate variability patterns such as the Pacific Decadal Oscillation and the El Nino Southern Oscillation (both climatic cycles that affect the amount of regional snowpack and river discharge). Natural sources of nutrients in the Columbia River include weathering, leaf litter, salmon carcasses, runoff from its tributaries, and ocean estuary exchange. Major anthropogenic impacts on nutrients in the basin are due to fertilizers from agriculture, sewage systems, logging, and the construction of dams.\nNutrient dynamics vary in the river basin from the headwaters to the main river and dams, to finally reaching the Columbia River estuary and ocean. Upstream in the headwaters, salmon runs are the main source of nutrients. Dams along the river impact nutrient cycling by increasing residence time of nutrients, and reducing the transport of silicate to the estuary, which directly impacts diatoms, a type of phytoplankton. The dams are also a barrier to salmon migration and can increase the amount of methane locally produced. The Columbia River estuary exports high rates of nutrients into the Pacific, except for nitrogen, which is delivered into the estuary by ocean upwelling sources."]], "corpus": "wikipedia"}
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{"tstamp": 1725803550.8321, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1725803550.5255, "finish": 1725803550.8321, "ip": "", "conv_id": "05cf2ac7ab2245a1b59a99af01719f53", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "What is the specification of itel p65 phone", "output": [["What is the specification of itel p65 phone", "Itel Mobile\n\nitel Mobile is a Chinese mobile phone manufacturer company that was founded by Transsion Holdings Co Limited in March 2008.\nTheir products are mainly sold in various emerging markets, including parts of Africa, South Asia, Southeast Asia, Europe and Latin America."]], "corpus": "wikipedia"}
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{"tstamp": 1725803550.8321, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1725803550.5255, "finish": 1725803550.8321, "ip": "", "conv_id": "4d66db400b214f07be043702c5ad4092", "model_name": "GritLM/GritLM-7B", "prompt": "What is the specification of itel p65 phone", "output": [["What is the specification of itel p65 phone", "Itel Mobile\n\nitel Mobile is a Chinese mobile phone manufacturer company that was founded by Transsion Holdings Co Limited in March 2008.\nTheir products are mainly sold in various emerging markets, including parts of Africa, South Asia, Southeast Asia, Europe and Latin America."]], "corpus": "wikipedia"}
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{"tstamp": 1725803585.676, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1725803585.3521, "finish": 1725803585.676, "ip": "", "conv_id": "26b969ece26f450584d9d24e2114d438", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "What is shilajin", "output": [["What is shilajin", "Shilajit\n\nShilajit (; , 'conqueror of the rocks'), salajeet (), mumijo or mumlayi or mumie is an organic-mineral product of predominantly biological origin, formed in the mountains (in mountain crevices and caves).\nA blackish-brown powder or an exudate from high mountain rocks, often found in the Himalayas, the Pamir Mountains, Afghanistan, (primarily in Gorno-Badakhshan, Tajikistan), (Karakoram, Gilgit-Baltistan in Pakistan, Nepal, Bhutan, the Caucasus Mountains within Russia, Altai, Central Asia, Iran, Mongolia and in southern Peru, where it is called Andean shilajit. The peoples of the East used shilajit in folk and non-traditional (alternative) medicine (Ayurveda, Chinese, Tibetan). Shilajit is sold both in dry extract form and in dietary supplements.\nHistory\nSince ancient times, shilajit has been a folk medicine in Afghanistan, India, Iran, China, Pakistan, Nepal, Central Asia and Tibet. Shilajit has been used as a folk medicine and in alternative medicine for more than four thousand years. The healing effects of shilajit for different diseases is mentioned in the works of Aristotle, Razi, Biruni, Ibn Sina and others.\nD'Herbelot, in his 1821 publication, stated that the Persians used the substance called mumiay, or mummy, as a potent cure-all to address broken bones and disease."]], "corpus": "wikipedia"}
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{"tstamp": 1725803585.676, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1725803585.3521, "finish": 1725803585.676, "ip": "", "conv_id": "d2dc3833b2e546949f3ab8622f72d693", "model_name": "text-embedding-004", "prompt": "What is shilajin", "output": [["What is shilajin", "Shinjū\n\nis a Japanese term meaning \"double suicide\", used in common parlance to refer to any group suicide of two or more individuals bound by love, typically lovers, parents and children, and even whole families. A double suicide without consent is called and it is considered as a sort of murder–suicide.\nLovers committing double suicide believed that they would be united again in heaven, a view supported by feudal teaching in Edo period Japan, which taught that the bond between two lovers is continued into the next world, and by the teaching of Pure Land Buddhism wherein it is believed that through double suicide, one can approach rebirth in the Pure Land."]], "corpus": "wikipedia"}
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{"tstamp": 1725803624.6675, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1725803622.2083, "finish": 1725803624.6675, "ip": "", "conv_id": "e3a925c1db7345798195cff86e92db6c", "model_name": "voyage-multilingual-2", "prompt": "What is the most powerful explosion in the universe ", "output": [["What is the most powerful explosion in the universe ", "Explosion\n\nAstronomical Among the largest known explosions in the universe are supernovae, which occur after the end of life of some types of stars. Solar flares are an example of common, much less energetic, explosions on the Sun, and presumably on most other stars as well. The energy source for solar flare activity comes from the tangling of magnetic field lines resulting from the rotation of the Sun's conductive plasma. Another type of large astronomical explosion occurs when a meteoroid or an asteroid impacts the surface of another object, or explodes in its atmosphere, such as a planet. This occurs because the two objects are moving at very high speed relative to each other (a minimum of for an Earth impacting body). For example, the Tunguska event of 1908 is believed to have resulted from a meteor air burst.\nBlack hole mergers, likely involving binary black hole systems, are capable of radiating many solar masses of energy into the universe in a fraction of a second, in the form of a gravitational wave. This is capable of transmitting ordinary energy and destructive forces to nearby objects, but in the vastness of space, nearby objects are rare. The gravitational wave observed on 21 May 2019, known as GW190521, produced a merger signal of about 100 ms duration, during which time is it estimated to have radiated away nine solar masses in the form of gravitational energy.\nChemical\nThe most common artificial explosives are chemical explosives, usually involving a rapid and violent oxidation reaction that produces large amounts of hot gas. Gunpowder was the first explosive to be invented and put to use. Other notable early developments in chemical explosive technology were Frederick Augustus Abel's development of nitrocellulose in 1865 and Alfred Nobel's invention of dynamite in 1866. Chemical explosions (both intentional and accidental) are often initiated by an electric spark or flame in the presence of oxygen. Accidental explosions may occur in fuel tanks, rocket engines, etc."]], "corpus": "wikipedia"}
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{"tstamp": 1725803624.6675, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1725803622.2083, "finish": 1725803624.6675, "ip": "", "conv_id": "f287126ac2024db18f89fd75ffe13132", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "What is the most powerful explosion in the universe ", "output": [["What is the most powerful explosion in the universe ", "Explosion\n\nAstronomical Among the largest known explosions in the universe are supernovae, which occur after the end of life of some types of stars. Solar flares are an example of common, much less energetic, explosions on the Sun, and presumably on most other stars as well. The energy source for solar flare activity comes from the tangling of magnetic field lines resulting from the rotation of the Sun's conductive plasma. Another type of large astronomical explosion occurs when a meteoroid or an asteroid impacts the surface of another object, or explodes in its atmosphere, such as a planet. This occurs because the two objects are moving at very high speed relative to each other (a minimum of for an Earth impacting body). For example, the Tunguska event of 1908 is believed to have resulted from a meteor air burst.\nBlack hole mergers, likely involving binary black hole systems, are capable of radiating many solar masses of energy into the universe in a fraction of a second, in the form of a gravitational wave. This is capable of transmitting ordinary energy and destructive forces to nearby objects, but in the vastness of space, nearby objects are rare. The gravitational wave observed on 21 May 2019, known as GW190521, produced a merger signal of about 100 ms duration, during which time is it estimated to have radiated away nine solar masses in the form of gravitational energy.\nChemical\nThe most common artificial explosives are chemical explosives, usually involving a rapid and violent oxidation reaction that produces large amounts of hot gas. Gunpowder was the first explosive to be invented and put to use. Other notable early developments in chemical explosive technology were Frederick Augustus Abel's development of nitrocellulose in 1865 and Alfred Nobel's invention of dynamite in 1866. Chemical explosions (both intentional and accidental) are often initiated by an electric spark or flame in the presence of oxygen. Accidental explosions may occur in fuel tanks, rocket engines, etc."]], "corpus": "wikipedia"}
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{"tstamp": 1725803678.8248, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1725803676.2852, "finish": 1725803678.8248, "ip": "", "conv_id": "42be1d4e98cc4c8e8a82746d9b799d05", "model_name": "GritLM/GritLM-7B", "prompt": "Give me the specification of Samsung galaxy S24 ultra ", "output": [["Give me the specification of Samsung galaxy S24 ultra ", "Samsung Galaxy S24\n\nCamera The Galaxy S24 and S24+ have a 50 MP wide sensor, a 10 MP 3x tele telephoto sensor and a 12 MP ultrawide sensor. The S24 Ultra has a 200 MP wide sensor, 50 MP 5× tele periscope telephoto sensor, 10 MP 3x tele telephoto sensor, and a 12 MP ultrawide sensor. The front camera uses a 12 MP sensor on all three models.\nBatteries\nThe Galaxy S24, S24+, and S24 Ultra contain non-removable 4,000 mAh, 4,900 mAh, and 5,000 mAh Li-ion batteries respectively. The S24 only charges at 25 watts, while the S24+ and S24 Ultra charge up at 45 watts charging.\nConnectivity\nThe Galaxy S24, and S24+ support 5G SA/NSA/Sub6, Wi-Fi 6E, and Bluetooth 5.3 connectivity, while the Galaxy S24 Ultra additionally supports Wi-Fi 7 and ultra-wideband.\nMemory and storage\nThe Galaxy S24 phones feature 4,800 MT/s LPDDR5X memory and Universal Flash Storage 3.1 with 128 GB or version 4.0 with 256 GB and above\nSoftware\nThe Samsung Galaxy S24 phones were released with Android 14 with Samsung's One UI 6.1 user experience. Samsung has promised 7 years of security patches and OS upgrades similar to that of Apple's.\nThey use Google's on-device Gemini Nano, already used by their own Pixel 8 Pro.\nReception\nHot S Pen\nSome users noted that the Galaxy S24 Ultra's S Pen \"absolutely reeks\", with a smell that was likened to \"burnt plastic\". A moderator on Samsung's EU forums explained,"]], "corpus": "wikipedia"}
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{"tstamp": 1725803678.8248, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1725803676.2852, "finish": 1725803678.8248, "ip": "", "conv_id": "d68e7726dc8e42ef857f4058f12be152", "model_name": "BM25", "prompt": "Give me the specification of Samsung galaxy S24 ultra ", "output": [["Give me the specification of Samsung galaxy S24 ultra ", "Samsung Galaxy S24\n\nCamera The Galaxy S24 and S24+ have a 50 MP wide sensor, a 10 MP 3x tele telephoto sensor and a 12 MP ultrawide sensor. The S24 Ultra has a 200 MP wide sensor, 50 MP 5× tele periscope telephoto sensor, 10 MP 3x tele telephoto sensor, and a 12 MP ultrawide sensor. The front camera uses a 12 MP sensor on all three models.\nBatteries\nThe Galaxy S24, S24+, and S24 Ultra contain non-removable 4,000 mAh, 4,900 mAh, and 5,000 mAh Li-ion batteries respectively. The S24 only charges at 25 watts, while the S24+ and S24 Ultra charge up at 45 watts charging.\nConnectivity\nThe Galaxy S24, and S24+ support 5G SA/NSA/Sub6, Wi-Fi 6E, and Bluetooth 5.3 connectivity, while the Galaxy S24 Ultra additionally supports Wi-Fi 7 and ultra-wideband.\nMemory and storage\nThe Galaxy S24 phones feature 4,800 MT/s LPDDR5X memory and Universal Flash Storage 3.1 with 128 GB or version 4.0 with 256 GB and above\nSoftware\nThe Samsung Galaxy S24 phones were released with Android 14 with Samsung's One UI 6.1 user experience. Samsung has promised 7 years of security patches and OS upgrades similar to that of Apple's.\nThey use Google's on-device Gemini Nano, already used by their own Pixel 8 Pro.\nReception\nHot S Pen\nSome users noted that the Galaxy S24 Ultra's S Pen \"absolutely reeks\", with a smell that was likened to \"burnt plastic\". A moderator on Samsung's EU forums explained,"]], "corpus": "wikipedia"}
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