Scheduled Commit

data/retrieval_battle-23cee96f-6768-4f6e-9a62-131dedf90618.jsonl

@@ -23,3 +23,4 @@
 {"tstamp": 1732229181.4518, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "20db37a161514b11b283c68bd80cc4dc", "0_model_name": "BM25", "0_prompt": "Baddeley's model of working memory", "0_output": [["Baddeley's model of working memory", "Baddeley's model of working memory\n\nBaddeley's model of working memory is a model of human memory proposed by Alan Baddeley and Graham Hitch in 1974, in an attempt to present a more accurate model of primary memory (often referred to as short-term memory). Working memory splits primary memory into multiple components, rather than considering it to be a single, unified construct."]], "0_corpus": "wikipedia", "1_conv_id": "6231a029cd7e4e5d9230459b8b696129", "1_model_name": "voyage-multilingual-2", "1_prompt": "Baddeley's model of working memory", "1_output": [["Baddeley's model of working memory", "Baddeley's model of working memory\n\nBaddeley's model of working memory is a model of human memory proposed by Alan Baddeley and Graham Hitch in 1974, in an attempt to present a more accurate model of primary memory (often referred to as short-term memory). Working memory splits primary memory into multiple components, rather than considering it to be a single, unified construct."]], "1_corpus": "wikipedia"}
 {"tstamp": 1732229213.5931, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "76a162d3df624890bfe6c2bf7eea9fc4", "0_model_name": "Salesforce/SFR-Embedding-2_R", "0_prompt": "working memory", "0_output": [["working memory", "Working memory\n\nWorking memory is a cognitive system with a limited capacity that can hold information temporarily. It is important for reasoning and the guidance of decision-making and behavior. Working memory is often used synonymously with short-term memory, but some theorists consider the two forms of memory distinct, assuming that working memory allows for the manipulation of stored information, whereas short-term memory only refers to the short-term storage of information. Working memory is a theoretical concept central to cognitive psychology, neuropsychology, and neuroscience."]], "0_corpus": "wikipedia", "1_conv_id": "c665709b31e74993a2f6ee6bc2bb9edc", "1_model_name": "BM25", "1_prompt": "working memory", "1_output": [["working memory", "Short-term memory\n\nSynaptic theory Various researchers have proposed that stimuli are coded in short-term memory using transmitter depletion. According to this hypothesis, a stimulus activates a spatial pattern of activity across neurons in a brain region. As these neurons fire, the available neurotransmitters are depleted. This depletion pattern represents stimulus information and functions as a memory trace. The memory trace decays over time as a consequence of neurotransmitter reuptake mechanisms that restore neurotransmitters to prior levels.\nRelationship with working memory\nThe relationship between short-term memory and working memory is described by various theories, but the two concepts are generally considered distinct. Neither holds information for long, but short-term memory is a simple store, while working memory allows it to be manipulated. Short-term memory is part of working memory, but is not the same thing.\nWorking memory refers to structures and processes used for temporarily storing and manipulating information. Working memory has been termed working attention. Working memory and attention together play a major role in the thought process. Short-term memory in general refers to the short-term storage of information, and it does not encompass memory manipulation or organization. Thus, while short-term memory components appear in working memory models, the concept of short-term memory is distinct from other concepts.\nWithin Baddeley's influential 1986 model of working memory two short-term storage mechanisms appear: the phonological loop and the visuospatial sketchpad. Most of the above research involves the phonological loop, because most of the work on short-term memory uses verbal material. Since the 1990s, however, research on visual short-term memory and spatial short-term memory has expanded."]], "1_corpus": "wikipedia"}
 {"tstamp": 1732229236.4019, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "4ea8c34aed2a42279cc3947c86c7dca6", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "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": "2d2b2eac434f46fe8d041a72ad07f30f", "1_model_name": "text-embedding-004", "1_prompt": "Which test was devised to determine whether robots can think?", "1_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "1_corpus": "wikipedia"}
+{"tstamp": 1732229280.7403, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "c4fe433b9e924218ba5f0ce0e7a634c2", "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": "fd4a9e8e095e4736bacd093811311cd4", "1_model_name": "embed-english-v3.0", "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: 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."]], "1_corpus": "arxiv"}

data/retrieval_individual-23cee96f-6768-4f6e-9a62-131dedf90618.jsonl

@@ -74,3 +74,11 @@
 {"tstamp": 1732229231.2282, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1732229230.9398, "finish": 1732229231.2282, "ip": "", "conv_id": "2d2b2eac434f46fe8d041a72ad07f30f", "model_name": "text-embedding-004", "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": 1732229264.1403, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1732229263.8026, "finish": 1732229264.1403, "ip": "", "conv_id": "c4fe433b9e924218ba5f0ce0e7a634c2", "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"}
 {"tstamp": 1732229264.1403, "task_type": "retrieval", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1732229263.8026, "finish": 1732229264.1403, "ip": "", "conv_id": "fd4a9e8e095e4736bacd093811311cd4", "model_name": "embed-english-v3.0", "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"}
+{"tstamp": 1732229375.3863, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1732229375.2208, "finish": 1732229375.3863, "ip": "", "conv_id": "2b4508e804e94ddda9e712d7d622c451", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "I am looking for a paper to using AI based applications to improve writing skills of foreign language learners", "output": [["I am looking for a paper to using AI based applications to improve writing skills of foreign language learners", "Title: Systematic Review for AI-based Language Learning Tools\n\nAbstract: The Second Language Acquisition field has been significantly impacted by a greater emphasis on individualized learning and rapid developments in artificial intelligence (AI). Although increasingly adaptive language learning tools are being developed with the application of AI to the Computer Assisted Language Learning field, there have been concerns regarding insufficient information and teacher preparation. To effectively utilize these tools, teachers need an in-depth overview on recently developed AI-based language learning tools. Therefore, this review synthesized information on AI tools that were developed between 2017 and 2020. A majority of these tools utilized machine learning and natural language processing, and were used to identify errors, provide feedback, and assess language abilities. After using these tools, learners demonstrated gains in their language abilities and knowledge. This review concludes by presenting pedagogical implications and emerging themes in the future research of AI-based language learning tools."]], "corpus": "arxiv"}
+{"tstamp": 1732229375.3863, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1732229375.2208, "finish": 1732229375.3863, "ip": "", "conv_id": "06a431a154b649ce8f1f2de0a967e424", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "I am looking for a paper to using AI based applications to improve writing skills of foreign language learners", "output": [["I am looking for a paper to using AI based applications to improve writing skills of foreign language learners", "Title: Exploring AI-Generated Text in Student Writing: How Does AI Help?\n\nAbstract: English as foreign language_EFL_students' use of text generated from artificial intelligence_AI_natural language generation_NLG_tools may improve their writing quality. However, it remains unclear to what extent AI-generated text in these students' writing might lead to higher-quality writing. We explored 23 Hong Kong secondary school students' attempts to write stories comprising their own words and AI-generated text. Human experts scored the stories for dimensions of content, language and organization. We analyzed the basic organization and structure and syntactic complexity of the stories' AI-generated text and performed multiple linear regression and cluster analyses. The results show the number of human words and the number of AI-generated words contribute significantly to scores. Besides, students can be grouped into competent and less competent writers who use more AI-generated text or less AI-generated text compared to their peers. Comparisons of clusters reveal some benefit of AI-generated text in improving the quality of both high-scoring students' and low-scoring students' writing. The findings can inform pedagogical strategies to use AI-generated text for EFL students' writing and to address digital divides. This study contributes designs of NLG tools and writing activities to implement AI-generated text in schools."]], "corpus": "arxiv"}
+{"tstamp": 1732229435.888, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1732229435.7893, "finish": 1732229435.888, "ip": "", "conv_id": "c411d615472b47dabc93722bf88c0a78", "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?", "Title: Can I say, now machines can think?\n\nAbstract: Generative AI techniques have opened the path for new generations of machines in diverse domains. These machines have various capabilities for example, they can produce images, generate answers or stories, and write codes based on the \"prompts\" only provided by users. These machines are considered 'thinking minds' because they have the ability to generate human-like responses. In this study, we have analyzed and explored the capabilities of artificial intelligence-enabled machines. We have revisited on Turing's concept of thinking machines and compared it with recent technological advancements. The objections and consequences of the thinking machines are also discussed in this study, along with available techniques to evaluate machines' cognitive capabilities. We have concluded that Turing Test is a critical aspect of evaluating machines' ability. However, there are other aspects of intelligence too, and AI machines exhibit most of these aspects."]], "corpus": "arxiv"}
+{"tstamp": 1732229435.888, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1732229435.7893, "finish": 1732229435.888, "ip": "", "conv_id": "a1d08f88101144a6a94762377cbb6641", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Title: \"Turing Tests\" For An AI Scientist\n\nAbstract: While LLMs have shown impressive capabilities in solving math or coding problems, the ability to make scientific discoveries remains a distinct challenge. This paper proposes a \"Turing test for an AI scientist\" to assess whether an AI agent can conduct scientific research independently, without relying on human-generated knowledge. Drawing inspiration from the historical development of science, we propose seven benchmark tests that evaluate an AI agent's ability to make groundbreaking discoveries in various scientific domains. These tests include inferring the heliocentric model from celestial observations, discovering the laws of motion in a simulated environment, deriving the differential equation governing vibrating strings, inferring Maxwell's equations from electrodynamics simulations, inventing numerical methods for initial value problems, discovering Huffman coding for data compression, and developing efficient sorting algorithms. To ensure the validity of these tests, the AI agent is provided with interactive libraries or datasets specific to each problem, without access to human knowledge that could potentially contain information about the target discoveries. The ultimate goal is to create an AI scientist capable of making novel and impactful scientific discoveries, surpassing the best human experts in their respective fields. These \"Turing tests\" serve as intermediate milestones, assessing the AI agent's ability to make discoveries that were groundbreaking in their time. If an AI agent can pass the majority of these seven tests, it would indicate significant progress towards building an AI scientist, paving the way for future advancements in autonomous scientific discovery. This paper aims to establish a benchmark for the capabilities of AI in scientific research and to stimulate further research in this exciting field."]], "corpus": "arxiv"}
+{"tstamp": 1732229486.4409, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1732229484.2875, "finish": 1732229486.4409, "ip": "", "conv_id": "5da672ecc57d4aaca67bb39d36e1e9b3", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "Which test was devised to determine whether aliens are real?", "output": [["Which test was devised to determine whether aliens are real?", "Alien abduction claimants\n\nMany repeat-abductees report that, as children from the ages of 2–6, they would be visited by balls of light that would enter their room at night. These balls would seem to play games with children and fly around the room. Some have interpreted them as being a way for the alleged abductees to develop their psychic abilities the way a physical ball helps develop coordination and athletic abilities. As such these intangible orbs have been dubbed \"psychic toys\". Although these phantasms are alleged to have appeared regularly, no corroborating sightings from members of the abductees' families or others that may have been expected to see them have been reported.\nHopkins Image Recognition Test\nUFO and abduction researcher Budd Hopkins has designed an image recognition test (known as the Hopkins Image Recognition Test, or HIRT) for children that he claims is helpful in verifying legitimate occurrences of alien abduction. There are ten different illustrated flash cards in the HIRT, nine of which depict \"images from myth, from the real world, and from popular culture\". The tenth image is the \"grey\" type entity commonly associated with claims of abduction. The images are drawn simply in black and white, featuring characters drawn in what Hopkins calls a \"neutral and inexpressive\" fashion. This is supposed to be a preventative measure intended to keep the images from affecting the child's responses on an emotional basis."]], "corpus": "wikipedia"}
+{"tstamp": 1732229486.4409, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1732229484.2875, "finish": 1732229486.4409, "ip": "", "conv_id": "64ca6c20f6354cbe892abcd848b140f5", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "Which test was devised to determine whether aliens are real?", "output": [["Which test was devised to determine whether aliens are real?", "Mirror test\n\nThe mirror test—sometimes called the mark test, mirror self-recognition (MSR) test, red spot technique, or rouge test—is a behavioral technique developed in 1970 by American psychologist Gordon Gallup Jr. as an attempt to determine whether an animal possesses the ability of visual self-recognition. The MSR test is the traditional method for attempting to measure physiological and cognitive self-awareness. However, agreement has been reached that animals can be self-aware in ways not measured by the mirror test, such as distinguishing between their own and others' songs and scents, and being aware of their own bodies, while humans have abnormally good vision, and thus intelligence that is highly visual.\nIn the classic MSR test, an animal is anesthetized and then marked (e.g. paint or sticker) on an area of the body the animal normally cannot see (e.g. forehead). When the animal recovers from the anesthetic, it is given access to a mirror. If the animal then touches or investigates the mark, it is taken as an indication that the animal perceives the reflected image as an image of itself, rather than of another animal."]], "corpus": "wikipedia"}
+{"tstamp": 1732229574.36, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1732229574.2564, "finish": 1732229574.36, "ip": "", "conv_id": "cb4a72d2941f45a2820077c0f06639ca", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "Can peoples memory be altered with news on televisions and newspapers", "output": [["Can peoples memory be altered with news on televisions and newspapers", "Title: Postmortem memory of public figures in news and social media\n\nAbstract: Deceased public figures are often said to live on in collective memory. We quantify this phenomenon by tracking mentions of 2,362 public figures in English-language online news and social media (Twitter) one year before and after death. We measure the sharp spike and rapid decay of attention following death and model collective memory as a composition of communicative and cultural memory. Clustering reveals four patterns of post-mortem memory, and regression analysis shows that boosts in media attention are largest for pre-mortem popular anglophones who died a young, unnatural death; that long-term boosts are smallest for leaders and largest for artists; and that, while both the news and Twitter are triggered by young and unnatural deaths, the news additionally curates collective memory when old persons or leaders die. Overall, we illuminate the age-old question who is remembered by society, and the distinct roles of news and social media in collective memory formation."]], "corpus": "arxiv"}
+{"tstamp": 1732229574.36, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1732229574.2564, "finish": 1732229574.36, "ip": "", "conv_id": "c727d7a9dae2498c9dc4f26efb4e7d0a", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "Can peoples memory be altered with news on televisions and newspapers", "output": [["Can peoples memory be altered with news on televisions and newspapers", "Title: Postmortem memory of public figures in news and social media\n\nAbstract: Deceased public figures are often said to live on in collective memory. We quantify this phenomenon by tracking mentions of 2,362 public figures in English-language online news and social media (Twitter) one year before and after death. We measure the sharp spike and rapid decay of attention following death and model collective memory as a composition of communicative and cultural memory. Clustering reveals four patterns of post-mortem memory, and regression analysis shows that boosts in media attention are largest for pre-mortem popular anglophones who died a young, unnatural death; that long-term boosts are smallest for leaders and largest for artists; and that, while both the news and Twitter are triggered by young and unnatural deaths, the news additionally curates collective memory when old persons or leaders die. Overall, we illuminate the age-old question who is remembered by society, and the distinct roles of news and social media in collective memory formation."]], "corpus": "arxiv"}

data/retrieval_side_by_side-23cee96f-6768-4f6e-9a62-131dedf90618.jsonl

@@ -4,3 +4,5 @@
 {"tstamp": 1732192845.4907, "task_type": "retrieval", "type": "rightvote", "models": ["Alibaba-NLP/gte-Qwen2-7B-instruct", "text-embedding-004"], "ip": "", "0_conv_id": "a6cb87308b31447385e823ae4a9f3791", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": "were the tartars the same as the khazaks, who did they come from", "0_output": [["were the tartars the same as the khazaks, who did they come from", "Tatar confederation\n\nWhen listing the 20 Turkic tribes, Kashgari also included non-Turks such as Kumo Xi, Khitans, Tanguts, and Chinese (the last one rendered as  < Karakhanid *Tawğaç). In the extant manuscript's text, the Tatars are located west of the Kyrgyzes; however, the manuscript's world-map shows that the Tatars were located west of the Ili river and west of the Bashkirs, whom Kashagari already located west of Tatars. Claus Schönig attributed such contradictions to errors made when the text and the map were copied. Kashgari additionally noted that Tatars were bilingual, speaking Turkic alongside their own languages; the same for the Yabaqus, Basmïls, and Chömüls. Yet available evidence suggested that the Yabaqus, Basmïls, and Chömüls were all Turkic speakers; therefore, Mehmet Fuat Köprülü concludes that in the 11th century, the Yabaqus, Basmïls, Chömüls, Qays and Tatars – the last two of whom Köprülü considers to be Turkified Mongols – could speak Kashgari's Karakhanid dialect as well as their own Turkic dialects, yet those peoples' own dialects differed from Karakhanid so substantially that Kashgari considered them other languages.\nAccording to Klyashtorny, the name \"Tatar\" was the Turkic designation for Mongols. As Ushnitsky writes, the ethnonym \"Tatar\" was used by the Turks only to designate \"strangers\", that is, peoples who did not speak Turkic languages. The Turkic tribes living among their Mongol-speaking neighbors were also called \"tat\" or \"tat-ar\". According to Bartold, the peoples of Mongolian origin who spoke the Mongolian language had always called themselves Tatars. Subsequently, this word was completely supplanted by the word \"Mongol\".\nHistory\nThe Rourans, Tatars' putative ancestors, roamed modern-day Mongolia in summer and crossed the Gobi desert southwards in winter in search of pastures. Rourans founded their Khaganate in the 5th century, around 402 CE. Among the Rourans' subjects were the Ashina tribe, who overthrew their Rouran overlords in 552 and annihilated the Rourans in 555. One branch of the dispersed Rourans migrated to the Greater Khingan mountain range where they renamed themselves after Tantan, a historical Khagan, and gradually incorporated themselves into the Shiwei tribal complex and emerged as 大室韋 Da (Great) Shiwei."]], "0_corpus": "wikipedia", "1_conv_id": "d669df71f26848d483e35512304df970", "1_model_name": "text-embedding-004", "1_prompt": "were the tartars the same as the khazaks, who did they come from", "1_output": [["were the tartars the same as the khazaks, who did they come from", "Tatars\n\nWhile also speaking languages belonging to different Kipchak sub-groups, genetic studies have shown that the three main groups of Tatars (Volga, Crimean, Siberian) do not have common ancestors and, thus, their formation occurred independently of one another. However, it is possible that all Tatar groups have at least partially the same origin, mainly from the times of the Golden Horde.\nMany noble families in the Tsardom of Russia and Russian Empire had Tatar origins.\nEtymology\nTatar became a name for populations of the former Golden Horde in Europe, such as those of the former Kazan, Crimean, Astrakhan, Qasim, and Siberian Khanates. The form Tartar has its origins in either Latin or French, coming to Western European languages from Turkish and the Persian language (, \"mounted messenger\"). From the beginning, the extra r was present in the Western forms and according to the Oxford English Dictionary this was most likely due to an association with Tartarus.\nThe Persian word is first recorded in the 13th century in reference to the hordes of Genghis Khan and is of unknown origin; according to the Oxford English Dictionary it is \"said to be\" ultimately from tata. The Arabic word for Tatars is . Tatars themselves wrote their name as  or ."]], "1_corpus": "wikipedia"}
 {"tstamp": 1732192870.1296, "task_type": "retrieval", "type": "leftvote", "models": ["Alibaba-NLP/gte-Qwen2-7B-instruct", "text-embedding-004"], "ip": "", "0_conv_id": "c932e7190d3748e982768c908bde2b41", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": "the republic of Ragusa appeared when and how?", "0_output": [["the republic of Ragusa appeared when and how?", "Republic of Ragusa\n\nThe Republic of Ragusa (; ; ; ; ) was an aristocratic maritime republic centered on the city of Dubrovnik (Ragusa in Italian and Latin; Raguxa in Venetian) in South Dalmatia (today in southernmost Croatia) that carried that name from 1358 until 1808. It reached its commercial peak in the 15th and the 16th centuries, before being conquered by Napoleon's French Empire and formally annexed by the Napoleonic Kingdom of Italy in 1808. It had a population of about 30,000 people, of whom 5,000 lived within the city walls. Its motto was \"\", a Latin phrase which can be translated as \"Liberty is not well sold for all the gold\".\nNames\nOriginally named  (Latin for \"Ragusan municipality\" or \"community\"), in the 14th century it was renamed  (Latin for Ragusan Republic), first mentioned in 1385. It was nevertheless a Republic under its previous name, although its Rector was appointed by Venice rather than by Ragusa's own Major Council. In Italian it is called ; in Croatian it is called  ().\nThe Slavic name Dubrovnik is derived from the word , an oak grove; by a folk etymology. The name Dubrovnik of the Adriatic city is first recorded in the Charter of Ban Kulin (1189). It came into use alongside Ragusa as early as the 14th century."]], "0_corpus": "wikipedia", "1_conv_id": "f98b49ec971448b8b12d2b5deaa86ad0", "1_model_name": "text-embedding-004", "1_prompt": "the republic of Ragusa appeared when and how?", "1_output": [["the republic of Ragusa appeared when and how?", "Ragusa, Sicily\n\nThe ancient city, located on a  hill, came into contact with nearby Greek colonies, and grew thanks to the nearby port of Camerina. After a short period of Carthaginian rule, it fell into the hands of the ancient Romans and the Byzantines, who fortified the city and built a large castle. Ragusa was occupied by the Arabs in 848 AD and remained under their rule until the 11th century, when the Normans conquered it. Ragusa was selected as a county seat, and its first count was Geoffrey, son of Count Ruggero of Sicily.\nThereafter, Ragusa's history followed the events of the Kingdom of Sicily, created in the first half of the twelfth century. A Chiaramonte family fief, it remained the county capital after it has been unified with Modica in 1296, a status that it lost in the 15th century after a popular revolt.\nIn 1693, Ragusa was devastated by a huge earthquake, which killed some 5,000 inhabitants. After the catastrophe, the city was largely rebuilt, and many Baroque buildings from that time remain in the city. Most of the population moved to a new settlement in the former district of Patro. The new municipality was called \"Ragusa Superiore\" (Upper Ragusa) and the ancient city \"Ragusa Inferiore\" (Lower Ragusa). Both cities remained separated until 1926, when they were merged to become a provincial capital in 1927 at the expense of Modica, which had been the former capital and the most populous and important city in the region since 1296.\nIn 1838, an asphalt deposit was discovered, which is still being worked."]], "1_corpus": "wikipedia"}
 {"tstamp": 1732192902.8791, "task_type": "retrieval", "type": "leftvote", "models": ["Alibaba-NLP/gte-Qwen2-7B-instruct", "text-embedding-004"], "ip": "", "0_conv_id": "4fda192af4c643d4bb040599641b6b1f", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": "when searching a legal document what should be looked out for", "0_output": [["when searching a legal document what should be looked out for", "Legal writing\n\nThe drafting of legal documents such as contracts is different as, unlike in most other legal writing categories, it is common to use language and clauses that are derived from form books, legal opinions and other documents without attribution. Lawyers use forms documents when drafting documents such as contracts, wills, and judgments. The key difference between using phrases or paragraphs from other legal documents, and copying in other contexts or copying the entire document, arises from the fact that lawyers are effectively drawing upon a common pool of clauses that they adjust and modify for their own purposes.\nPlain language movement\nThe Plain Language Movement in legal writing involves an effort to avoid complex language and terminology in legal documents, to make legal writing more understandable and accessible. One of the goals of the movement is to reduce reliance on terms of art, words that have a specific meaning within the context of the law, but that may carry a different meaning in other contexts.\nLegalese\nLegalese is an English term first used in 1914 for legal writing that is very difficult for laymen to read and understand, the implication being that this abstruseness is deliberate for excluding the legally untrained and to justify high fees. Legalese, as a term, has been adopted in other languages. Legalese is characterized by long sentences, many modifying clauses, complex vocabulary, high abstraction, and insensitivity to the layman's need to understand the document's gist. Legalese arises most commonly in legal drafting, yet appears in both types of legal analysis.\nSome important points in the debate of \"legalese\" v. \"plain language\" as the continued standard for legal writing include:"]], "0_corpus": "wikipedia", "1_conv_id": "91966d131dcf4e1bbcb1aa62475a3958", "1_model_name": "text-embedding-004", "1_prompt": "when searching a legal document what should be looked out for", "1_output": [["when searching a legal document what should be looked out for", "Legal research\n\nThe processes of legal research vary according to the country and the legal system involved. Legal research involves tasks such as:\nFinding primary sources of law, or primary authority, in a given jurisdiction. The main primary sources of law include constitutions, case law, statutes, and regulations.\nSearching secondary authority for background information about legal topics.  Secondary authorities can come in many forms, such as law reviews, legal dictionaries, legal treatises, and legal encyclopedias such as American Jurisprudence and Corpus Juris Secundum.\nSearching non-legal sources for investigative or supporting factual information.\nLegal research is performed by anyone with a need for legal information, including lawyers, law librarians, and paralegals. Sources of legal information can include printed books, free legal research websites (like Cornell Law School's Legal Information Institute, Findlaw.com, Martindale Hubbell, or CanLII), and websites or software providing paid access to legal research databases such as Wolters Kluwer, LexisNexis, Westlaw, Lex Intell, VLex, and Bloomberg Law. Law libraries around the world provide research services to help their patrons in law schools, law firms, and other research environments find the legal information they need. Many law libraries and institutions provide free access to legal information on the web, either individually or via collective action, such as with the Free Access to Law Movement.\nDatabases and software tools"]], "1_corpus": "wikipedia"}
+{"tstamp": 1732229406.6839, "task_type": "retrieval", "type": "tievote", "models": ["mixedbread-ai/mxbai-embed-large-v1", "nomic-ai/nomic-embed-text-v1.5"], "ip": "", "0_conv_id": "2b4508e804e94ddda9e712d7d622c451", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": "I am looking for a paper to using AI based applications to improve writing skills of foreign language learners", "0_output": [["I am looking for a paper to using AI based applications to improve writing skills of foreign language learners", "Title: Systematic Review for AI-based Language Learning Tools\n\nAbstract: The Second Language Acquisition field has been significantly impacted by a greater emphasis on individualized learning and rapid developments in artificial intelligence (AI). Although increasingly adaptive language learning tools are being developed with the application of AI to the Computer Assisted Language Learning field, there have been concerns regarding insufficient information and teacher preparation. To effectively utilize these tools, teachers need an in-depth overview on recently developed AI-based language learning tools. Therefore, this review synthesized information on AI tools that were developed between 2017 and 2020. A majority of these tools utilized machine learning and natural language processing, and were used to identify errors, provide feedback, and assess language abilities. After using these tools, learners demonstrated gains in their language abilities and knowledge. This review concludes by presenting pedagogical implications and emerging themes in the future research of AI-based language learning tools."]], "0_corpus": "arxiv", "1_conv_id": "06a431a154b649ce8f1f2de0a967e424", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "I am looking for a paper to using AI based applications to improve writing skills of foreign language learners", "1_output": [["I am looking for a paper to using AI based applications to improve writing skills of foreign language learners", "Title: Exploring AI-Generated Text in Student Writing: How Does AI Help?\n\nAbstract: English as foreign language_EFL_students' use of text generated from artificial intelligence_AI_natural language generation_NLG_tools may improve their writing quality. However, it remains unclear to what extent AI-generated text in these students' writing might lead to higher-quality writing. We explored 23 Hong Kong secondary school students' attempts to write stories comprising their own words and AI-generated text. Human experts scored the stories for dimensions of content, language and organization. We analyzed the basic organization and structure and syntactic complexity of the stories' AI-generated text and performed multiple linear regression and cluster analyses. The results show the number of human words and the number of AI-generated words contribute significantly to scores. Besides, students can be grouped into competent and less competent writers who use more AI-generated text or less AI-generated text compared to their peers. Comparisons of clusters reveal some benefit of AI-generated text in improving the quality of both high-scoring students' and low-scoring students' writing. The findings can inform pedagogical strategies to use AI-generated text for EFL students' writing and to address digital divides. This study contributes designs of NLG tools and writing activities to implement AI-generated text in schools."]], "1_corpus": "arxiv"}
+{"tstamp": 1732229513.5394, "task_type": "retrieval", "type": "leftvote", "models": ["mixedbread-ai/mxbai-embed-large-v1", "nomic-ai/nomic-embed-text-v1.5"], "ip": "", "0_conv_id": "5da672ecc57d4aaca67bb39d36e1e9b3", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": "Which test was devised to determine whether aliens are real?", "0_output": [["Which test was devised to determine whether aliens are real?", "Alien abduction claimants\n\nMany repeat-abductees report that, as children from the ages of 2–6, they would be visited by balls of light that would enter their room at night. These balls would seem to play games with children and fly around the room. Some have interpreted them as being a way for the alleged abductees to develop their psychic abilities the way a physical ball helps develop coordination and athletic abilities. As such these intangible orbs have been dubbed \"psychic toys\". Although these phantasms are alleged to have appeared regularly, no corroborating sightings from members of the abductees' families or others that may have been expected to see them have been reported.\nHopkins Image Recognition Test\nUFO and abduction researcher Budd Hopkins has designed an image recognition test (known as the Hopkins Image Recognition Test, or HIRT) for children that he claims is helpful in verifying legitimate occurrences of alien abduction. There are ten different illustrated flash cards in the HIRT, nine of which depict \"images from myth, from the real world, and from popular culture\". The tenth image is the \"grey\" type entity commonly associated with claims of abduction. The images are drawn simply in black and white, featuring characters drawn in what Hopkins calls a \"neutral and inexpressive\" fashion. This is supposed to be a preventative measure intended to keep the images from affecting the child's responses on an emotional basis."]], "0_corpus": "wikipedia", "1_conv_id": "64ca6c20f6354cbe892abcd848b140f5", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "Which test was devised to determine whether aliens are real?", "1_output": [["Which test was devised to determine whether aliens are real?", "Mirror test\n\nThe mirror test—sometimes called the mark test, mirror self-recognition (MSR) test, red spot technique, or rouge test—is a behavioral technique developed in 1970 by American psychologist Gordon Gallup Jr. as an attempt to determine whether an animal possesses the ability of visual self-recognition. The MSR test is the traditional method for attempting to measure physiological and cognitive self-awareness. However, agreement has been reached that animals can be self-aware in ways not measured by the mirror test, such as distinguishing between their own and others' songs and scents, and being aware of their own bodies, while humans have abnormally good vision, and thus intelligence that is highly visual.\nIn the classic MSR test, an animal is anesthetized and then marked (e.g. paint or sticker) on an area of the body the animal normally cannot see (e.g. forehead). When the animal recovers from the anesthetic, it is given access to a mirror. If the animal then touches or investigates the mark, it is taken as an indication that the animal perceives the reflected image as an image of itself, rather than of another animal."]], "1_corpus": "wikipedia"}