Scheduled Commit

data/clustering_battle-128277df-5553-4ba0-b3e1-4ac131791901.jsonl

@@ -15,3 +15,4 @@
 {"tstamp": 1740255808.3222, "task_type": "clustering", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "3008bf481ea54e3cb502155c539ad898", "0_model_name": "embed-english-v3.0", "0_prompt": ["PlayStation", "Xbox", "Atari", "Nintendo", "Sega", "theocracy", "democracy", "republic", "oligarchy", "monarchy", "C++", "JavaScript", "Go"], "0_ncluster": 3, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "c0f2a3c81d0148608a0e6451df1d251f", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": ["PlayStation", "Xbox", "Atari", "Nintendo", "Sega", "theocracy", "democracy", "republic", "oligarchy", "monarchy", "C++", "JavaScript", "Go"], "1_ncluster": 3, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
 {"tstamp": 1740255866.6935, "task_type": "clustering", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "5928ef5dd9e54d3e9ec4eebedc813f57", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": ["Asia", "Antarctica", "gas", "solid", "Chihuahua", "Beagle", "German Shepherd", "Bulldog", "Labrador", "Aries", "Gemini", "Scorpio", "Libra"], "0_ncluster": 4, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "66bb9dd5718d4c2d8d07cfc3bb88a153", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": ["Asia", "Antarctica", "gas", "solid", "Chihuahua", "Beagle", "German Shepherd", "Bulldog", "Labrador", "Aries", "Gemini", "Scorpio", "Libra"], "1_ncluster": 4, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
 {"tstamp": 1740336565.3236, "task_type": "clustering", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "4aed5347d2d74004a10333efa3388914", "0_model_name": "GritLM/GritLM-7B", "0_prompt": ["i need a hat", "what is this hat color", "i want a coat", "what's this coat material", "i want coupons", "vouchers", "looking for a battery", "human"], "0_ncluster": 2, "0_output": "", "0_ndim": "2D (press for 3D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "97ee2c31e8dc4fd990105e570cbb7aaf", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_prompt": ["i need a hat", "what is this hat color", "i want a coat", "what's this coat material", "i want coupons", "vouchers", "looking for a battery", "human"], "1_ncluster": 2, "1_output": "", "1_ndim": "2D (press for 3D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
+{"tstamp": 1740345034.9777, "task_type": "clustering", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "53d0a233b26741fe94a4d38632cae160", "0_model_name": "GritLM/GritLM-7B", "0_prompt": ["Orion", "Leo", "Taurus", "Cygnus", "Ursa Major", "Cassiopeia", "Scorpius", "McDonald's", "Taco Bell", "Burger King", "Edge", "Firefox", "Brave", "Opera"], "0_ncluster": 3, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "7199db4df842400d93c4842e52e2fea0", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": ["Orion", "Leo", "Taurus", "Cygnus", "Ursa Major", "Cassiopeia", "Scorpius", "McDonald's", "Taco Bell", "Burger King", "Edge", "Firefox", "Brave", "Opera"], "1_ncluster": 3, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}

data/clustering_individual-128277df-5553-4ba0-b3e1-4ac131791901.jsonl

@@ -66,3 +66,5 @@
 {"tstamp": 1740336490.4113, "task_type": "clustering", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1740336490.3355, "finish": 1740336490.4113, "ip": "", "conv_id": "97ee2c31e8dc4fd990105e570cbb7aaf", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": ["i need a hat", "what is this hat color", "i want a coat", "what's this coat material", "i want coupons", "vouchers", "looking for a battery"], "ncluster": 1, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1740336527.8581, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1740336527.7513, "finish": 1740336527.8581, "ip": "", "conv_id": "4aed5347d2d74004a10333efa3388914", "model_name": "GritLM/GritLM-7B", "prompt": ["i need a hat", "what is this hat color", "i want a coat", "what's this coat material", "i want coupons", "vouchers", "looking for a battery", "human"], "ncluster": 2, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1740336527.8581, "task_type": "clustering", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1740336527.7513, "finish": 1740336527.8581, "ip": "", "conv_id": "97ee2c31e8dc4fd990105e570cbb7aaf", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": ["i need a hat", "what is this hat color", "i want a coat", "what's this coat material", "i want coupons", "vouchers", "looking for a battery", "human"], "ncluster": 2, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1740345020.3472, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1740345020.2222, "finish": 1740345020.3472, "ip": "", "conv_id": "53d0a233b26741fe94a4d38632cae160", "model_name": "GritLM/GritLM-7B", "prompt": ["Orion", "Leo", "Taurus", "Cygnus", "Ursa Major", "Cassiopeia", "Scorpius", "McDonald's", "Taco Bell", "Burger King", "Edge", "Firefox", "Brave", "Opera"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1740345020.3472, "task_type": "clustering", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1740345020.2222, "finish": 1740345020.3472, "ip": "", "conv_id": "7199db4df842400d93c4842e52e2fea0", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": ["Orion", "Leo", "Taurus", "Cygnus", "Ursa Major", "Cassiopeia", "Scorpius", "McDonald's", "Taco Bell", "Burger King", "Edge", "Firefox", "Brave", "Opera"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}

data/retrieval_battle-128277df-5553-4ba0-b3e1-4ac131791901.jsonl

@@ -9,3 +9,4 @@
 {"tstamp": 1740336083.531, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "7773343232294417a3d54611f33cbea5", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe test was introduced by Turing in his 1950 paper \"Computing Machinery and Intelligence\" while working at the University of Manchester. It opens with the words: \"I propose to consider the question, 'Can machines think? Because \"thinking\" is difficult to define, Turing chooses to \"replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\" Turing describes the new form of the problem in terms of a three-person game called the \"imitation game\", in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: \"Are there imaginable digital computers which would do well in the imitation game?\" This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that \"machines can think\".\nSince Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a \"mind\", \"understanding\", or \"consciousness\", regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.\nHistory"]], "0_corpus": "wikipedia", "1_conv_id": "123f8be342cc46c0866d86fe97d0148e", "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": 1740336182.5629, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "7278a23a1799483490f974434c07c5e7", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": "something that explain how citation in llm works ", "0_output": [["something that explain how citation in llm works ", "Title: When Large Language Models Meet Citation: A Survey\n\nAbstract: Citations in scholarly work serve the essential purpose of acknowledging and crediting the original sources of knowledge that have been incorporated or referenced. Depending on their surrounding textual context, these citations are used for different motivations and purposes. Large Language Models (LLMs) could be helpful in capturing these fine-grained citation information via the corresponding textual context, thereby enabling a better understanding towards the literature. Furthermore, these citations also establish connections among scientific papers, providing high-quality inter-document relationships and human-constructed knowledge. Such information could be incorporated into LLMs pre-training and improve the text representation in LLMs. Therefore, in this paper, we offer a preliminary review of the mutually beneficial relationship between LLMs and citation analysis. Specifically, we review the application of LLMs for in-text citation analysis tasks, including citation classification, citation-based summarization, and citation recommendation. We then summarize the research pertinent to leveraging citation linkage knowledge to improve text representations of LLMs via citation prediction, network structure information, and inter-document relationship. We finally provide an overview of these contemporary methods and put forth potential promising avenues in combining LLMs and citation analysis for further investigation."]], "0_corpus": "arxiv", "1_conv_id": "e99b80b304334bdc967d01814168ae2d", "1_model_name": "BM25", "1_prompt": "something that explain how citation in llm works ", "1_output": [["something that explain how citation in llm works ", "Title: Explaining Relationships Among Research Papers\n\nAbstract: Due to the rapid pace of research publications, keeping up to date with all the latest related papers is very time-consuming, even with daily feed tools. There is a need for automatically generated, short, customized literature reviews of sets of papers to help researchers decide what to read. While several works in the last decade have addressed the task of explaining a single research paper, usually in the context of another paper citing it, the relationship among multiple papers has been ignored; prior works have focused on generating a single citation sentence in isolation, without addressing the expository and transition sentences needed to connect multiple papers in a coherent story. In this work, we explore a feature-based, LLM-prompting approach to generate richer citation texts, as well as generating multiple citations at once to capture the complex relationships among research papers. We perform an expert evaluation to investigate the impact of our proposed features on the quality of the generated paragraphs and find a strong correlation between human preference and integrative writing style, suggesting that humans prefer high-level, abstract citations, with transition sentences between them to provide an overall story."]], "1_corpus": "arxiv"}
 {"tstamp": 1740340162.8147, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "7ff0c6a9096f4edebd336a8257975e01", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": "Find me a planet near α 23h 06m 29.368s and δ −05° 02′ 29.04″ that could be habitable.", "0_output": [["Find me a planet near α 23h 06m 29.368s and δ −05° 02′ 29.04″ that could be habitable.", "List of nearest terrestrial exoplanet candidates\n\nOn August 24, 2016, astronomers announced the discovery of a rocky planet in the habitable zone of Proxima Centauri, the closest star to Earth (not counting the Sun). Called Proxima b, the planet is 1.3 times the mass of Earth and has an orbital period of roughly 11.2 Earth days. However, Proxima Centauri's classification as a red dwarf casts doubts on the habitability of any exoplanets in its orbit due to low stellar flux, high probability of tidal locking, small circumstellar habitable zones and high stellar variation. Another likely candidate is Alpha Centauri, Earth's nearest Sun-like star system 4.37 light-years away. Estimates place the probability of finding a habitable planet around Alpha Centauri A or B at roughly 75%. Alpha Centauri is the target of several exoplanet-finding missions, including Breakthrough Starshot and Mission Centaur, the latter of which is chronicled in the 2016 documentary film The Search for Earth Proxima.\nData Table\nNote: There is no scientific consensus about terrestrial composition of most of the planets in the list. Sources in the \"Main source\" column confirm the possibility of terrestrial composition.\nIn September 2012, the discovery of two planets orbiting Gliese 163 was announced.  One of the planets, Gliese 163 c, about 6.9 times the mass of Earth and somewhat hotter, was considered to be within the habitable zone, but is probably not terrestrial.\nIn May 2016, the finding of three Earth-like planets of ultracool dwarf TRAPPIST-1 has been released.\nThe existence of the planet Gliese 832 c was refuted in 2022, when a study found that the radial velocity signal shows characteristics of a signal originating from stellar activity, and not from a planet.\nStatistics\nNote: in most cases the composition of the atmosphere and atmosphere pressure of exoplanets are unknown, so surface temperatures are estimates based on computer models and expert opinions."]], "0_corpus": "wikipedia", "1_conv_id": "36b2ff5699a74777b117f150860eaccb", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_prompt": "Find me a planet near α 23h 06m 29.368s and δ −05° 02′ 29.04″ that could be habitable.", "1_output": [["Find me a planet near α 23h 06m 29.368s and δ −05° 02′ 29.04″ that could be habitable.", "Proxima Centauri b\n\nProxima Centauri b (or Proxima b), sometimes referred to as Alpha Centauri Cb, is an exoplanet orbiting within the habitable zone of the red dwarf star Proxima Centauri, which is the closest star to the Sun and part of the larger triple star system Alpha Centauri. It is about  from Earth in the constellation Centaurus, making it and Proxima d, along with the currently disputed Proxima c, the closest known exoplanets to the Solar System."]], "1_corpus": "wikipedia"}
+{"tstamp": 1740344906.9526, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "872fe26e24204b83b4c293531a85f2cc", "0_model_name": "text-embedding-3-large", "0_prompt": "I want to know what bees and the traveling salesman have in common", "0_output": [["I want to know what bees and the traveling salesman have in common", "Travelling salesman problem\n\nA 2011 study in animal cognition titled \"Let the Pigeon Drive the Bus,\" named after the children's book Don't Let the Pigeon Drive the Bus!, examined spatial cognition in pigeons by studying their flight patterns between multiple feeders in a laboratory in relation to the travelling salesman problem. In the first experiment, pigeons were placed in the corner of a lab room and allowed to fly to nearby feeders containing peas. The researchers found that pigeons largely used proximity to determine which feeder they would select next. In the second experiment, the feeders were arranged in such a way that flying to the nearest feeder at every opportunity would be largely inefficient if the pigeons needed to visit every feeder. The results of the second experiment indicate that pigeons, while still favoring proximity-based solutions, \"can plan several steps ahead along the route when the differences in travel costs between efficient and less efficient routes based on proximity become larger.\" These results are consistent with other experiments done with non-primates, which have proven that some non-primates were able to plan complex travel routes. This suggests non-primates may possess a relatively sophisticated spatial cognitive ability.\nNatural computation\nWhen presented with a spatial configuration of food sources, the amoeboid Physarum polycephalum adapts its morphology to create an efficient path between the food sources, which can also be viewed as an approximate solution to TSP.\nBenchmarks\nFor benchmarking of TSP algorithms, TSPLIB is a library of sample instances of the TSP and related problems is maintained; see the TSPLIB external reference. Many of them are lists of actual cities and layouts of actual printed circuits.\nPopular culture\nTravelling Salesman, by director Timothy Lanzone, is the story of four mathematicians hired by the U.S. government to solve the most elusive problem in computer-science history: P vs. NP.\nSolutions to the problem are used by mathematician Robert A. Bosch in a subgenre called TSP art."]], "0_corpus": "wikipedia", "1_conv_id": "c42772b378674df79c6fd13dbf34042f", "1_model_name": "text-embedding-004", "1_prompt": "I want to know what bees and the traveling salesman have in common", "1_output": [["I want to know what bees and the traveling salesman have in common", "Bee\n\nW. B. Yeats's poem The Lake Isle of Innisfree (1888) contains the couplet \"Nine bean rows will I have there, a hive for the honey bee, / And live alone in the bee loud glade.\" At the time he was living in Bedford Park in the West of London. Beatrix Potter's illustrated book The Tale of Mrs Tittlemouse (1910) features Babbity Bumble and her brood (pictured). Kit Williams' treasure hunt book The Bee on the Comb (1984) uses bees and beekeeping as part of its story and puzzle. Sue Monk Kidd's The Secret Life of Bees (2004), and the 2009 film starring Dakota Fanning, tells the story of a girl who escapes her abusive home and finds her way to live with a family of beekeepers, the Boatwrights.\nThe 2007 animated comedy film Bee Movie used Jerry Seinfeld's first script and was his first work for children; he starred as a bee named Barry B. Benson, alongside Renée Zellweger. Critics found its premise awkward and its delivery tame. Dave Goulson's A Sting in the Tale (2014) describes his efforts to save bumblebees in Britain, as well as much about their biology. The playwright Laline Paull's fantasy The Bees (2015) tells the tale of a hive bee named Flora 717 from hatching onwards.\nBeekeeping\nHumans have kept honey bee colonies, commonly in hives, for millennia. Beekeepers collect honey, beeswax, propolis, pollen, and royal jelly from hives; bees are also kept to pollinate crops and to produce bees for sale to other beekeepers."]], "1_corpus": "wikipedia"}