Scheduled Commit

data/retrieval_battle-f36c9f97-e795-4522-8eb7-7dee254b42c7.jsonl

@@ -71,3 +71,6 @@
 {"tstamp": 1723677877.6234, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "82754d487652489fbb253f2a882437d0", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": "What is a whore?", "0_output": [["What is a whore?", "Slut\n\nSlut (archaic: slattern) is an English-language term for a person, usually a woman, who is sexually promiscuous or considered to have loose sexual morals. It is predominately used as an insult, sexual slur or offensive term of disparagement. It originally meant \"a dirty, slovenly woman\", and is rarely used to refer to men, generally requiring clarification by use of the terms male slut or man whore.\nThe word was used as early as the 1300s (in the form of an adjective, sluttish, referring to a  untidy appearance) by Geoffrey Chaucer in The Canterbury Tales.\nFrom the late 20th century, there have been attempts to reclaim the word, exemplified by various SlutWalk parades, and some individuals embrace the title as a source of pride.\nEtymology, common usages and synonyms\nThe common denotative meanings of slut are 'sexually promiscuous woman', or 'immoral or dissolute woman; prostitute'. These definitions identify a slut as a woman of low character—a person who lacks the ability or chooses not to exercise a power of discernment to order her affairs. Similar terms used for men are cad, rake, male slut, man whore, himbo, womanizer, stud, and player. The adjective slutty carries a similar connotation, but can be applied both to people and to clothing and accessories, such as Halloween costumes."]], "0_corpus": "wikipedia", "1_conv_id": "45abcf21090e4a5db51dbb3a52035524", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "What is a whore?", "1_output": [["What is a whore?", "Prostitution\n\nProstitution is the business or practice of engaging in sexual activity in exchange for payment. The definition of \"sexual activity\" varies, and is often defined as an activity requiring physical contact (e.g., sexual intercourse, non-penetrative sex, manual sex, oral sex, etc.) with the customer. The requirement of physical contact also creates the risk of transferring infections. Prostitution is sometimes described as sexual services, commercial sex or, colloquially, hooking. It is sometimes referred to euphemistically as \"the world's oldest profession\" in the English-speaking world. A person who works in the field is usually called a prostitute or sex worker, but other words, such as hooker, putana, or whore, are sometimes used pejoratively to refer to those who work as prostitutes."]], "1_corpus": "wikipedia"}
 {"tstamp": 1723678963.5839, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "ceb01b4394f847dd9741923773d92a79", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe 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": "369e07a563f04d2482df8cb858e4db0e", "1_model_name": "GritLM/GritLM-7B", "1_prompt": "Which test was devised to determine whether robots can think?", "1_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe test was introduced by Turing in his 1950 paper \"Computing Machinery and Intelligence\" while working at the University of Manchester. It opens with the words: \"I propose to consider the question, 'Can machines think? Because \"thinking\" is difficult to define, Turing chooses to \"replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\" Turing describes the new form of the problem in terms of a three-person game called the \"imitation game\", in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: \"Are there imaginable digital computers which would do well in the imitation game?\" This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that \"machines can think\".\nSince Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a \"mind\", \"understanding\", or \"consciousness\", regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.\nHistory"]], "1_corpus": "wikipedia"}
 {"tstamp": 1723684271.8485, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "a66c7d7b0fa746c18b80f16fff823708", "0_model_name": "voyage-multilingual-2", "0_prompt": "what hangs from the trees in the bayou", "0_output": [["what hangs from the trees in the bayou", "Big Thicket\n\nBaygalls (aka: bay-gallberry holly bogs in part; Acid bog baygalls): With dense undergrowth and twisted vines growing in stagnant, blackwater, acid bogs, under a shadowy canopy of swamp tupelo and cypress, baygalls are often said to exemplify and epitomize the Big Thicket. Baygalls typically form at the base of slopes where seeps, springs, and rainwater drain onto the margins of the flat floodplains and bottomlands, away from the main channels. The drainage from the slopes maintain saturated, muddy soils in these already damp and humid areas, where pools, bogs, and stagnant drainages form in depressions on the flat landscape. With sphagnum mosses and high peat content, baygall soils and water have very high acidity, pH levels of 4.5 are common.  The high acid levels are a significant factor in distinguishing baygalls from the floodplain and flats ecosystems. Although baygall waters are generally shallow and torpid, they sometimes form small, highly acidic blackwater streams, slowly moving into the larger creeks and bayous. Hanging bogs occur where water pools on irregular flat and low areas on the slopes above the bottomlands. The name baygall is derived from sweetbay magnolia (Magnolia virginiana) and sweet gallberry holly (Ilex coriacea). These and swamp titi (Cyrilla racemiflora) are dominant and other common shrubs include southern bayberry (Myrica cerifera), water willow (Decodon verticillatus), red bay (Persea borbonia), and Virginia sweetspire (Itea virginica). Vines like muscadine grape (Vitis rotundifolia), supplejack or rattan-vine (Berchemia scandens) can grow impressively large. Larger trees include swamp tupelo (Nyssa biflora) and bald cypress (Taxodium distichum). In addition to the prominent fragrant water-lily (Nymphaea odorata), baygalls harbor many small and obscure wildflowers such as four species of carnivorous bladderworts (Utricularia), and saprophytic species like burmannia (Burmannia biflora) and nodding-nixie (Apteria aphylla). Some baygalls and hanging bogs are no more than a small pool, while others can be a mile across. One author states that the Jack Gore Baygall Unit of the BTNP, at 12 square miles, is the largest baygall in the world."]], "0_corpus": "wikipedia", "1_conv_id": "16e53e0064c04bdda997efc0c377299f", "1_model_name": "GritLM/GritLM-7B", "1_prompt": "what hangs from the trees in the bayou", "1_output": [["what hangs from the trees in the bayou", "Big Thicket\n\nBaygalls (aka: bay-gallberry holly bogs in part; Acid bog baygalls): With dense undergrowth and twisted vines growing in stagnant, blackwater, acid bogs, under a shadowy canopy of swamp tupelo and cypress, baygalls are often said to exemplify and epitomize the Big Thicket. Baygalls typically form at the base of slopes where seeps, springs, and rainwater drain onto the margins of the flat floodplains and bottomlands, away from the main channels. The drainage from the slopes maintain saturated, muddy soils in these already damp and humid areas, where pools, bogs, and stagnant drainages form in depressions on the flat landscape. With sphagnum mosses and high peat content, baygall soils and water have very high acidity, pH levels of 4.5 are common.  The high acid levels are a significant factor in distinguishing baygalls from the floodplain and flats ecosystems. Although baygall waters are generally shallow and torpid, they sometimes form small, highly acidic blackwater streams, slowly moving into the larger creeks and bayous. Hanging bogs occur where water pools on irregular flat and low areas on the slopes above the bottomlands. The name baygall is derived from sweetbay magnolia (Magnolia virginiana) and sweet gallberry holly (Ilex coriacea). These and swamp titi (Cyrilla racemiflora) are dominant and other common shrubs include southern bayberry (Myrica cerifera), water willow (Decodon verticillatus), red bay (Persea borbonia), and Virginia sweetspire (Itea virginica). Vines like muscadine grape (Vitis rotundifolia), supplejack or rattan-vine (Berchemia scandens) can grow impressively large. Larger trees include swamp tupelo (Nyssa biflora) and bald cypress (Taxodium distichum). In addition to the prominent fragrant water-lily (Nymphaea odorata), baygalls harbor many small and obscure wildflowers such as four species of carnivorous bladderworts (Utricularia), and saprophytic species like burmannia (Burmannia biflora) and nodding-nixie (Apteria aphylla). Some baygalls and hanging bogs are no more than a small pool, while others can be a mile across. One author states that the Jack Gore Baygall Unit of the BTNP, at 12 square miles, is the largest baygall in the world."]], "1_corpus": "wikipedia"}
+{"tstamp": 1723685128.0326, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "b30cfb04c6b44968b9df1aa74e3e3b92", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "自動任務生成", "0_output": [["自動任務生成", "Title: Self-Organized Criticality Effect on Stability: Magneto-Thermal Oscillations in a Granular YBCO Superconductor\n\nAbstract: We show that the self-organized criticality of the Bean's state in each of the grains of a granular superconductor results in magneto-thermal oscillations preceding a series of subsequent flux jumps. We find that the frequency of these oscillations is proportional to the external magnetic field sweep rate and is inversely proportional to the square root of the heat capacity. We demonstrate experimentally and theoretically the universality of this dependence that is mainly influenced by the granularity of the superconductor."]], "0_corpus": "arxiv", "1_conv_id": "655f3460f24545069cc21f897dccaa2d", "1_model_name": "voyage-multilingual-2", "1_prompt": "自動任務生成", "1_output": [["自動任務生成", "Title: AutoAgents: A Framework for Automatic Agent Generation\n\nAbstract: Large language models (LLMs) have enabled remarkable advances in automated task-solving with multi-agent systems. However, most existing LLM-based multi-agent approaches rely on predefined agents to handle simple tasks, limiting the adaptability of multi-agent collaboration to different scenarios. Therefore, we introduce AutoAgents, an innovative framework that adaptively generates and coordinates multiple specialized agents to build an AI team according to different tasks. Specifically, AutoAgents couples the relationship between tasks and roles by dynamically generating multiple required agents based on task content and planning solutions for the current task based on the generated expert agents. Multiple specialized agents collaborate with each other to efficiently accomplish tasks. Concurrently, an observer role is incorporated into the framework to reflect on the designated plans and agents' responses and improve upon them. Our experiments on various benchmarks demonstrate that AutoAgents generates more coherent and accurate solutions than the existing multi-agent methods. This underscores the significance of assigning different roles to different tasks and of team cooperation, offering new perspectives for tackling complex tasks. The repository of this project is available at https://github.com/Link-AGI/AutoAgents."]], "1_corpus": "arxiv"}
+{"tstamp": 1723685201.3728, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "99c7605b2b3443e2af5d43d698bcb00e", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "automatic quest generation via LLM", "0_output": [["automatic quest generation via LLM", "Title: AutoManual: Generating Instruction Manuals by LLM Agents via Interactive Environmental Learning\n\nAbstract: Large Language Models (LLM) based agents have shown promise in autonomously completing tasks across various domains, e.g., robotics, games, and web navigation. However, these agents typically require elaborate design and expert prompts to solve tasks in specific domains, which limits their adaptability. We introduce AutoManual, a framework enabling LLM agents to autonomously build their understanding through interaction and adapt to new environments. AutoManual categorizes environmental knowledge into diverse rules and optimizes them in an online fashion by two agents: 1) The Planner codes actionable plans based on current rules for interacting with the environment. 2) The Builder updates the rules through a well-structured rule system that facilitates online rule management and essential detail retention. To mitigate hallucinations in managing rules, we introduce \\textit{case-conditioned prompting} strategy for the Builder. Finally, the Formulator agent compiles these rules into a comprehensive manual. The self-generated manual can not only improve the adaptability but also guide the planning of smaller LLMs while being human-readable. Given only one simple demonstration, AutoManual significantly improves task success rates, achieving 97.4\\% with GPT-4-turbo and 86.2\\% with GPT-3.5-turbo on ALFWorld benchmark tasks. The source code will be available soon."]], "0_corpus": "arxiv", "1_conv_id": "bef5e273ea5b4f33a0cc0d0a09b0933a", "1_model_name": "BM25", "1_prompt": "automatic quest generation via LLM", "1_output": [["automatic quest generation via LLM", "Title: EvoAgent: Towards Automatic Multi-Agent Generation via Evolutionary Algorithms\n\nAbstract: The rise of powerful large language models (LLMs) has spurred a new trend in building LLM-based autonomous agents for solving complex tasks, especially multi-agent systems. Despite the remarkable progress, we notice that existing works are heavily dependent on human-designed frameworks, which greatly limits the functional scope and scalability of agent systems. How to automatically extend the specialized agent to multi-agent systems to improve task-solving capability still remains a significant challenge. In this paper, we introduce EvoAgent, a generic method to automatically extend expert agents to multi-agent systems via the evolutionary algorithm, thereby improving the effectiveness of LLM-based agents in solving tasks. Specifically, we consider the existing agent frameworks as the initial individual and then apply a series of evolutionary operators (e.g., mutation, crossover, selection, etc.) to generate multiple agents with diverse agent settings. EvoAgent can be generalized to any LLM-based agent framework, and can automatically extend the existing agent framework to multi-agent systems without any extra human designs. Experimental results across various tasks have shown that EvoAgent can automatically generate multiple expert agents and significantly enhance the task-solving capabilities of LLM-based agents."]], "1_corpus": "arxiv"}
+{"tstamp": 1723685249.8148, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "57ba620d45ff4bc39ff0987f4d32c3ea", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": "automatic game quest generation with LLM", "0_output": [["automatic game quest generation with LLM", "Title: Automatic Bug Detection in LLM-Powered Text-Based Games Using LLMs\n\nAbstract: Advancements in large language models (LLMs) are revolutionizing interactive game design, enabling dynamic plotlines and interactions between players and non-player characters (NPCs). However, LLMs may exhibit flaws such as hallucinations, forgetfulness, or misinterpretations of prompts, causing logical inconsistencies and unexpected deviations from intended designs. Automated techniques for detecting such game bugs are still lacking. To address this, we propose a systematic LLM-based method for automatically identifying such bugs from player game logs, eliminating the need for collecting additional data such as post-play surveys. Applied to a text-based game DejaBoom!, our approach effectively identifies bugs inherent in LLM-powered interactive games, surpassing unstructured LLM-powered bug-catching methods and filling the gap in automated detection of logical and design flaws."]], "0_corpus": "arxiv", "1_conv_id": "82985ff41a6c430db9049ecac2edbe89", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_prompt": "automatic game quest generation with LLM", "1_output": [["automatic game quest generation with LLM", "Title: Game Generation via Large Language Models\n\nAbstract: Recently, the emergence of large language models (LLMs) has unlocked new opportunities for procedural content generation. However, recent attempts mainly focus on level generation for specific games with defined game rules such as Super Mario Bros. and Zelda. This paper investigates the game generation via LLMs. Based on video game description language, this paper proposes an LLM-based framework to generate game rules and levels simultaneously. Experiments demonstrate how the framework works with prompts considering different combinations of context. Our findings extend the current applications of LLMs and offer new insights for generating new games in the area of procedural content generation."]], "1_corpus": "arxiv"}

data/retrieval_individual-f36c9f97-e795-4522-8eb7-7dee254b42c7.jsonl

@@ -350,3 +350,12 @@
 {"tstamp": 1723684254.3571, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1723684251.9068, "finish": 1723684254.3571, "ip": "", "conv_id": "a66c7d7b0fa746c18b80f16fff823708", "model_name": "voyage-multilingual-2", "prompt": "what hangs from the trees in the bayou", "output": [["what hangs from the trees in the bayou", "Big Thicket\n\nBaygalls (aka: bay-gallberry holly bogs in part; Acid bog baygalls): With dense undergrowth and twisted vines growing in stagnant, blackwater, acid bogs, under a shadowy canopy of swamp tupelo and cypress, baygalls are often said to exemplify and epitomize the Big Thicket. Baygalls typically form at the base of slopes where seeps, springs, and rainwater drain onto the margins of the flat floodplains and bottomlands, away from the main channels. The drainage from the slopes maintain saturated, muddy soils in these already damp and humid areas, where pools, bogs, and stagnant drainages form in depressions on the flat landscape. With sphagnum mosses and high peat content, baygall soils and water have very high acidity, pH levels of 4.5 are common.  The high acid levels are a significant factor in distinguishing baygalls from the floodplain and flats ecosystems. Although baygall waters are generally shallow and torpid, they sometimes form small, highly acidic blackwater streams, slowly moving into the larger creeks and bayous. Hanging bogs occur where water pools on irregular flat and low areas on the slopes above the bottomlands. The name baygall is derived from sweetbay magnolia (Magnolia virginiana) and sweet gallberry holly (Ilex coriacea). These and swamp titi (Cyrilla racemiflora) are dominant and other common shrubs include southern bayberry (Myrica cerifera), water willow (Decodon verticillatus), red bay (Persea borbonia), and Virginia sweetspire (Itea virginica). Vines like muscadine grape (Vitis rotundifolia), supplejack or rattan-vine (Berchemia scandens) can grow impressively large. Larger trees include swamp tupelo (Nyssa biflora) and bald cypress (Taxodium distichum). In addition to the prominent fragrant water-lily (Nymphaea odorata), baygalls harbor many small and obscure wildflowers such as four species of carnivorous bladderworts (Utricularia), and saprophytic species like burmannia (Burmannia biflora) and nodding-nixie (Apteria aphylla). Some baygalls and hanging bogs are no more than a small pool, while others can be a mile across. One author states that the Jack Gore Baygall Unit of the BTNP, at 12 square miles, is the largest baygall in the world."]], "corpus": "wikipedia"}
 {"tstamp": 1723684254.3571, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1723684251.9068, "finish": 1723684254.3571, "ip": "", "conv_id": "16e53e0064c04bdda997efc0c377299f", "model_name": "GritLM/GritLM-7B", "prompt": "what hangs from the trees in the bayou", "output": [["what hangs from the trees in the bayou", "Big Thicket\n\nBaygalls (aka: bay-gallberry holly bogs in part; Acid bog baygalls): With dense undergrowth and twisted vines growing in stagnant, blackwater, acid bogs, under a shadowy canopy of swamp tupelo and cypress, baygalls are often said to exemplify and epitomize the Big Thicket. Baygalls typically form at the base of slopes where seeps, springs, and rainwater drain onto the margins of the flat floodplains and bottomlands, away from the main channels. The drainage from the slopes maintain saturated, muddy soils in these already damp and humid areas, where pools, bogs, and stagnant drainages form in depressions on the flat landscape. With sphagnum mosses and high peat content, baygall soils and water have very high acidity, pH levels of 4.5 are common.  The high acid levels are a significant factor in distinguishing baygalls from the floodplain and flats ecosystems. Although baygall waters are generally shallow and torpid, they sometimes form small, highly acidic blackwater streams, slowly moving into the larger creeks and bayous. Hanging bogs occur where water pools on irregular flat and low areas on the slopes above the bottomlands. The name baygall is derived from sweetbay magnolia (Magnolia virginiana) and sweet gallberry holly (Ilex coriacea). These and swamp titi (Cyrilla racemiflora) are dominant and other common shrubs include southern bayberry (Myrica cerifera), water willow (Decodon verticillatus), red bay (Persea borbonia), and Virginia sweetspire (Itea virginica). Vines like muscadine grape (Vitis rotundifolia), supplejack or rattan-vine (Berchemia scandens) can grow impressively large. Larger trees include swamp tupelo (Nyssa biflora) and bald cypress (Taxodium distichum). In addition to the prominent fragrant water-lily (Nymphaea odorata), baygalls harbor many small and obscure wildflowers such as four species of carnivorous bladderworts (Utricularia), and saprophytic species like burmannia (Burmannia biflora) and nodding-nixie (Apteria aphylla). Some baygalls and hanging bogs are no more than a small pool, while others can be a mile across. One author states that the Jack Gore Baygall Unit of the BTNP, at 12 square miles, is the largest baygall in the world."]], "corpus": "wikipedia"}
 {"tstamp": 1723684865.9797, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1723684863.3748, "finish": 1723684865.9797, "ip": "", "conv_id": "73eb57c95c4d4a63a27ffff4df412a4e", "model_name": "text-embedding-3-large", "prompt": "give me a happy person", "output": [["give me a happy person", "Happiness\n\nHappiness is a positive and pleasant emotion, ranging from contentment to intense joy. Moments of happiness may be triggered by positive life experiences or thoughts, but sometimes it may arise from no obvious cause. The level of happiness for longer periods of time is more strongly correlated with levels of life satisfaction, subjective well-being, flourishing and eudaimonia. In common usage, the word happy can be an appraisal of those measures themselves or as a shorthand for a \"source\" of happiness (for example, \"find happiness in life\" as in finding the meaning in life). As with any emotion, the precise definition of happiness has been a perennial debate in philosophy.\nDefinitions\n\"Happiness\" is subject to debate on usage and meaning, and on possible differences in understanding by culture.\nThe word is mostly used in relation to two factors:\nthe current experience of the feeling of an emotion (affect) such as pleasure or joy, or of a more general sense of 'emotional condition as a whole'. For instance Daniel Kahneman has defined happiness as \"what I experience here and now\". This usage is prevalent in dictionary definitions of happiness."]], "corpus": "wikipedia"}
+{"tstamp": 1723685027.1396, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1723685026.9013, "finish": 1723685027.1396, "ip": "", "conv_id": "dbe077a2e59c42d7ad8b8a6d33f0b0c0", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "解釋便利商店的職責", "output": [["解釋便利商店的職責", "Convenience store\n\nA convenience store, convenience shop, bodega, corner store or corner shop is a small retail store that stocks a range of everyday items such as tea, coffee, groceries, fruits, vegetables, snacks, confectionery, soft drinks, ice creams, tobacco products, lottery tickets, over-the-counter drugs, toiletries, newspapers and magazines.\nIn some jurisdictions, convenience stores are licensed to sell alcoholic drinks, although many jurisdictions limit such beverages to those with relatively low alcohol content, like beer and wine. The stores may also offer money order and wire transfer services, along with the use of a fax machine or photocopier for a small per-copy cost. Some also sell tickets or recharge smart cards, e.g. OPUS cards in Montreal, Canada or include a small deli. They differ from general stores and village shops in that they are not in a rural location and are used as a convenient (hence their common name) supplement to larger stores.\nA convenience store may be part of a gas/petrol station, so customers can purchase goods while refuelling their vehicle. It may be located alongside a busy road, in an urban area, near a railway or railroad station or other transport hub. In some countries, convenience stores have long shopping hours and some remain open 24 hours."]], "corpus": "wikipedia"}
+{"tstamp": 1723685064.05, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1723685063.8614, "finish": 1723685064.05, "ip": "", "conv_id": "488cbbf6fee8492dab29a92deba99423", "model_name": "GritLM/GritLM-7B", "prompt": "便利商店的職責", "output": [["便利商店的職責", "Title: Power Law of Customers' Expenditures in Convenience Stores\n\nAbstract: In a convenience store chain, a tail of the cumulative density function of the expenditure of a person during a single shopping trip follows a power law with an exponent of -2.5. The exponent is independent of the location of the store, the shopper's age, the day of week, and the time of day."]], "corpus": "arxiv"}
+{"tstamp": 1723685064.05, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1723685063.8614, "finish": 1723685064.05, "ip": "", "conv_id": "df1101ec298d480eadb28725cf3ae9e6", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "便利商店的職責", "output": [["便利商店的職責", "Title: JiuZhang: A Chinese Pre-trained Language Model for Mathematical Problem Understanding\n\nAbstract: This paper aims to advance the mathematical intelligence of machines by presenting the first Chinese mathematical pre-trained language model~(PLM) for effectively understanding and representing mathematical problems. Unlike other standard NLP tasks, mathematical texts are difficult to understand, since they involve mathematical terminology, symbols and formulas in the problem statement. Typically, it requires complex mathematical logic and background knowledge for solving mathematical problems. Considering the complex nature of mathematical texts, we design a novel curriculum pre-training approach for improving the learning of mathematical PLMs, consisting of both basic and advanced courses. Specially, we first perform token-level pre-training based on a position-biased masking strategy, and then design logic-based pre-training tasks that aim to recover the shuffled sentences and formulas, respectively. Finally, we introduce a more difficult pre-training task that enforces the PLM to detect and correct the errors in its generated solutions. We conduct extensive experiments on offline evaluation (including nine math-related tasks) and online $A/B$ test. Experimental results demonstrate the effectiveness of our approach compared with a number of competitive baselines. Our code is available at: \\textcolor{blue}{\\url{https://github.com/RUCAIBox/JiuZhang}}."]], "corpus": "arxiv"}
+{"tstamp": 1723685101.1933, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1723685099.0783, "finish": 1723685101.1933, "ip": "", "conv_id": "b30cfb04c6b44968b9df1aa74e3e3b92", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "自動任務生成", "output": [["自動任務生成", "Title: Self-Organized Criticality Effect on Stability: Magneto-Thermal Oscillations in a Granular YBCO Superconductor\n\nAbstract: We show that the self-organized criticality of the Bean's state in each of the grains of a granular superconductor results in magneto-thermal oscillations preceding a series of subsequent flux jumps. We find that the frequency of these oscillations is proportional to the external magnetic field sweep rate and is inversely proportional to the square root of the heat capacity. We demonstrate experimentally and theoretically the universality of this dependence that is mainly influenced by the granularity of the superconductor."]], "corpus": "arxiv"}
+{"tstamp": 1723685101.1933, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1723685099.0783, "finish": 1723685101.1933, "ip": "", "conv_id": "655f3460f24545069cc21f897dccaa2d", "model_name": "voyage-multilingual-2", "prompt": "自動任務生成", "output": [["自動任務生成", "Title: AutoAgents: A Framework for Automatic Agent Generation\n\nAbstract: Large language models (LLMs) have enabled remarkable advances in automated task-solving with multi-agent systems. However, most existing LLM-based multi-agent approaches rely on predefined agents to handle simple tasks, limiting the adaptability of multi-agent collaboration to different scenarios. Therefore, we introduce AutoAgents, an innovative framework that adaptively generates and coordinates multiple specialized agents to build an AI team according to different tasks. Specifically, AutoAgents couples the relationship between tasks and roles by dynamically generating multiple required agents based on task content and planning solutions for the current task based on the generated expert agents. Multiple specialized agents collaborate with each other to efficiently accomplish tasks. Concurrently, an observer role is incorporated into the framework to reflect on the designated plans and agents' responses and improve upon them. Our experiments on various benchmarks demonstrate that AutoAgents generates more coherent and accurate solutions than the existing multi-agent methods. This underscores the significance of assigning different roles to different tasks and of team cooperation, offering new perspectives for tackling complex tasks. The repository of this project is available at https://github.com/Link-AGI/AutoAgents."]], "corpus": "arxiv"}
+{"tstamp": 1723685156.5748, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1723685154.275, "finish": 1723685156.5748, "ip": "", "conv_id": "99c7605b2b3443e2af5d43d698bcb00e", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "automatic quest generation via LLM", "output": [["automatic quest generation via LLM", "Title: AutoManual: Generating Instruction Manuals by LLM Agents via Interactive Environmental Learning\n\nAbstract: Large Language Models (LLM) based agents have shown promise in autonomously completing tasks across various domains, e.g., robotics, games, and web navigation. However, these agents typically require elaborate design and expert prompts to solve tasks in specific domains, which limits their adaptability. We introduce AutoManual, a framework enabling LLM agents to autonomously build their understanding through interaction and adapt to new environments. AutoManual categorizes environmental knowledge into diverse rules and optimizes them in an online fashion by two agents: 1) The Planner codes actionable plans based on current rules for interacting with the environment. 2) The Builder updates the rules through a well-structured rule system that facilitates online rule management and essential detail retention. To mitigate hallucinations in managing rules, we introduce \\textit{case-conditioned prompting} strategy for the Builder. Finally, the Formulator agent compiles these rules into a comprehensive manual. The self-generated manual can not only improve the adaptability but also guide the planning of smaller LLMs while being human-readable. Given only one simple demonstration, AutoManual significantly improves task success rates, achieving 97.4\\% with GPT-4-turbo and 86.2\\% with GPT-3.5-turbo on ALFWorld benchmark tasks. The source code will be available soon."]], "corpus": "arxiv"}
+{"tstamp": 1723685156.5748, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1723685154.275, "finish": 1723685156.5748, "ip": "", "conv_id": "bef5e273ea5b4f33a0cc0d0a09b0933a", "model_name": "BM25", "prompt": "automatic quest generation via LLM", "output": [["automatic quest generation via LLM", "Title: EvoAgent: Towards Automatic Multi-Agent Generation via Evolutionary Algorithms\n\nAbstract: The rise of powerful large language models (LLMs) has spurred a new trend in building LLM-based autonomous agents for solving complex tasks, especially multi-agent systems. Despite the remarkable progress, we notice that existing works are heavily dependent on human-designed frameworks, which greatly limits the functional scope and scalability of agent systems. How to automatically extend the specialized agent to multi-agent systems to improve task-solving capability still remains a significant challenge. In this paper, we introduce EvoAgent, a generic method to automatically extend expert agents to multi-agent systems via the evolutionary algorithm, thereby improving the effectiveness of LLM-based agents in solving tasks. Specifically, we consider the existing agent frameworks as the initial individual and then apply a series of evolutionary operators (e.g., mutation, crossover, selection, etc.) to generate multiple agents with diverse agent settings. EvoAgent can be generalized to any LLM-based agent framework, and can automatically extend the existing agent framework to multi-agent systems without any extra human designs. Experimental results across various tasks have shown that EvoAgent can automatically generate multiple expert agents and significantly enhance the task-solving capabilities of LLM-based agents."]], "corpus": "arxiv"}
+{"tstamp": 1723685226.6387, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1723685226.5211, "finish": 1723685226.6387, "ip": "", "conv_id": "57ba620d45ff4bc39ff0987f4d32c3ea", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "automatic game quest generation with LLM", "output": [["automatic game quest generation with LLM", "Title: Automatic Bug Detection in LLM-Powered Text-Based Games Using LLMs\n\nAbstract: Advancements in large language models (LLMs) are revolutionizing interactive game design, enabling dynamic plotlines and interactions between players and non-player characters (NPCs). However, LLMs may exhibit flaws such as hallucinations, forgetfulness, or misinterpretations of prompts, causing logical inconsistencies and unexpected deviations from intended designs. Automated techniques for detecting such game bugs are still lacking. To address this, we propose a systematic LLM-based method for automatically identifying such bugs from player game logs, eliminating the need for collecting additional data such as post-play surveys. Applied to a text-based game DejaBoom!, our approach effectively identifies bugs inherent in LLM-powered interactive games, surpassing unstructured LLM-powered bug-catching methods and filling the gap in automated detection of logical and design flaws."]], "corpus": "arxiv"}
+{"tstamp": 1723685226.6387, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1723685226.5211, "finish": 1723685226.6387, "ip": "", "conv_id": "82985ff41a6c430db9049ecac2edbe89", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "automatic game quest generation with LLM", "output": [["automatic game quest generation with LLM", "Title: Game Generation via Large Language Models\n\nAbstract: Recently, the emergence of large language models (LLMs) has unlocked new opportunities for procedural content generation. However, recent attempts mainly focus on level generation for specific games with defined game rules such as Super Mario Bros. and Zelda. This paper investigates the game generation via LLMs. Based on video game description language, this paper proposes an LLM-based framework to generate game rules and levels simultaneously. Experiments demonstrate how the framework works with prompts considering different combinations of context. Our findings extend the current applications of LLMs and offer new insights for generating new games in the area of procedural content generation."]], "corpus": "arxiv"}

data/retrieval_single_choice-f36c9f97-e795-4522-8eb7-7dee254b42c7.jsonl

@@ -0,0 +1 @@
+{"tstamp": 1723685045.5241, "task_type": "retrieval", "type": "upvote", "models": "Alibaba-NLP/gte-Qwen2-7B-instruct", "ip": "", "conv_id": "dbe077a2e59c42d7ad8b8a6d33f0b0c0", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "解釋便利商店的職責", "output": [["解釋便利商店的職責", "Convenience store\n\nA convenience store, convenience shop, bodega, corner store or corner shop is a small retail store that stocks a range of everyday items such as tea, coffee, groceries, fruits, vegetables, snacks, confectionery, soft drinks, ice creams, tobacco products, lottery tickets, over-the-counter drugs, toiletries, newspapers and magazines.\nIn some jurisdictions, convenience stores are licensed to sell alcoholic drinks, although many jurisdictions limit such beverages to those with relatively low alcohol content, like beer and wine. The stores may also offer money order and wire transfer services, along with the use of a fax machine or photocopier for a small per-copy cost. Some also sell tickets or recharge smart cards, e.g. OPUS cards in Montreal, Canada or include a small deli. They differ from general stores and village shops in that they are not in a rural location and are used as a convenient (hence their common name) supplement to larger stores.\nA convenience store may be part of a gas/petrol station, so customers can purchase goods while refuelling their vehicle. It may be located alongside a busy road, in an urban area, near a railway or railroad station or other transport hub. In some countries, convenience stores have long shopping hours and some remain open 24 hours."]], "corpus": "wikipedia"}