Scheduled Commit

data/retrieval_battle-928d45d9-19ab-4d05-9aae-4b6574886d0d.jsonl

@@ -15,3 +15,6 @@
 {"tstamp": 1722540085.7613, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "b17be05f60b14e5fbdb47d124d1b5f4d", "0_model_name": "text-embedding-004", "0_prompt": "who has become the new chairman of sebi", "0_output": [["who has become the new chairman of sebi", "Securities and Exchange Board of India\n\nSEBI is managed by its board of members, which consist of the following people:\nThe chairman, who is nominated by the Union Government of India.\nTwo members from the Union Finance Ministry.\nOne member from the Reserve Bank of India.\nThe remaining five members are nominated by the Union Government of India, and out of them at least three should be whole-time members.\nAfter the amendment of 1999, collective investment schemes were brought under SEBI except nidhis, chit funds and cooperatives.\nOrganisation structure\nMadhabi Puri Buch took charge of chairman on 1 March 2022, replacing Ajay Tyagi, whose term ended on 28 February 2022. Madhabi Puri Buch is the first woman chairperson of SEBI.\nCurrent Board members\nThe board comprises:\nList of Chairpersons\nList of Chairmen:\nNational Apex Bodies\nNational Institute of Securities Markets of India\nFunctions and responsibilities\nThe Preamble of the Securities and Exchange Board of India describes the basic functions of the Securities and Exchange Board of India as \"...to protect the interests of investors in securities and to promote the development of, and to regulate the securities market and for matters connected there with or incidental there to\".\nSEBI has to be responsive to the needs of three groups, which constitute the market:\nissuers of securities\ninvestors"]], "0_corpus": "wikipedia", "1_conv_id": "6d4f9992bb314b7493d9a2023a107a14", "1_model_name": "GritLM/GritLM-7B", "1_prompt": "who has become the new chairman of sebi", "1_output": [["who has become the new chairman of sebi", "Securities and Exchange Board of India\n\nSEBI is managed by its board of members, which consist of the following people:\nThe chairman, who is nominated by the Union Government of India.\nTwo members from the Union Finance Ministry.\nOne member from the Reserve Bank of India.\nThe remaining five members are nominated by the Union Government of India, and out of them at least three should be whole-time members.\nAfter the amendment of 1999, collective investment schemes were brought under SEBI except nidhis, chit funds and cooperatives.\nOrganisation structure\nMadhabi Puri Buch took charge of chairman on 1 March 2022, replacing Ajay Tyagi, whose term ended on 28 February 2022. Madhabi Puri Buch is the first woman chairperson of SEBI.\nCurrent Board members\nThe board comprises:\nList of Chairpersons\nList of Chairmen:\nNational Apex Bodies\nNational Institute of Securities Markets of India\nFunctions and responsibilities\nThe Preamble of the Securities and Exchange Board of India describes the basic functions of the Securities and Exchange Board of India as \"...to protect the interests of investors in securities and to promote the development of, and to regulate the securities market and for matters connected there with or incidental there to\".\nSEBI has to be responsive to the needs of three groups, which constitute the market:\nissuers of securities\ninvestors"]], "1_corpus": "wikipedia"}
 {"tstamp": 1722540099.6479, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "3aa3450787a14567823dc40ac1682797", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": "who had created the second bank of the united states", "0_output": [["who had created the second bank of the united states", "Second Bank of the United States\n\nThe Second Bank of the United States was the second federally authorized Hamiltonian national bank in the United States. Located in Philadelphia, Pennsylvania, the bank was chartered from February 1816 to January 1836. The bank's formal name, according to section 9 of its charter as passed by Congress, was \"The President, Directors, and Company, of the Bank of the United States\". While other banks in the US were chartered by and only allowed to have branches in a single state, it was authorized to have branches in multiple states and lend money to the US government.\nA private corporation with public duties, the bank handled all fiscal transactions for the U.S. government, and was accountable to Congress and the U.S. Treasury. Twenty percent of its capital was owned by the federal government, the bank's single largest stockholder. Four thousand private investors held 80 percent of the bank's capital, including three thousand Europeans. The bulk of the stocks were held by a few hundred wealthy Americans. In its time, the institution was the largest monied corporation in the world.\nThe essential function of the bank was to regulate the public credit issued by private banking institutions through the fiscal duties it performed for the U.S. Treasury, and to establish a sound and stable national currency. The federal deposits endowed the bank with its regulatory capacity.\nModeled on Alexander Hamilton's First Bank of the United States, the Second Bank was chartered by President James Madison, who in 1791 had attacked the First Bank as unconstitutional, in 1816 and began operations at its main branch in Philadelphia on January 7, 1817, managing 25 branch offices nationwide by 1832."]], "0_corpus": "wikipedia", "1_conv_id": "f0ed429b581f4be9a8e5e03936c56a2f", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "who had created the second bank of the united states", "1_output": [["who had created the second bank of the united states", "Second Bank of the United States\n\nThe Second Bank of the United States was the second federally authorized Hamiltonian national bank in the United States. Located in Philadelphia, Pennsylvania, the bank was chartered from February 1816 to January 1836. The bank's formal name, according to section 9 of its charter as passed by Congress, was \"The President, Directors, and Company, of the Bank of the United States\". While other banks in the US were chartered by and only allowed to have branches in a single state, it was authorized to have branches in multiple states and lend money to the US government.\nA private corporation with public duties, the bank handled all fiscal transactions for the U.S. government, and was accountable to Congress and the U.S. Treasury. Twenty percent of its capital was owned by the federal government, the bank's single largest stockholder. Four thousand private investors held 80 percent of the bank's capital, including three thousand Europeans. The bulk of the stocks were held by a few hundred wealthy Americans. In its time, the institution was the largest monied corporation in the world.\nThe essential function of the bank was to regulate the public credit issued by private banking institutions through the fiscal duties it performed for the U.S. Treasury, and to establish a sound and stable national currency. The federal deposits endowed the bank with its regulatory capacity.\nModeled on Alexander Hamilton's First Bank of the United States, the Second Bank was chartered by President James Madison, who in 1791 had attacked the First Bank as unconstitutional, in 1816 and began operations at its main branch in Philadelphia on January 7, 1817, managing 25 branch offices nationwide by 1832."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722545235.0702, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "dd217c99aeff4ddf8bfff289fbb393cd", "0_model_name": "embed-english-v3.0", "0_prompt": "the vast interior rural area of australia is known as the", "0_output": [["the vast interior rural area of australia is known as the", "Outback\n\nThe Outback is a remote, vast, sparsely populated area of Australia. The Outback is more remote than the bush. While often envisaged as being arid, the Outback regions extend from the northern to southern Australian coastlines and encompass a number of climatic zones, including tropical and monsoonal climates in northern areas, arid areas in the \"red centre\" and semi-arid and temperate climates in southerly regions. The total population is estimated at 607,000 people.\nGeographically, the Outback is unified by a combination of factors, most notably a low human population density, a largely intact natural environment and, in many places, low-intensity land uses, such as pastoralism (livestock grazing) in which production is reliant on the natural environment. The Outback is deeply ingrained in Australian heritage, history and folklore. In Australian art the subject of the Outback has been vogue, particularly in the 1940s. In 2009, as part of the Q150 celebrations, the Queensland Outback was announced as one of the Q150 Icons of Queensland for its role as a \"natural attraction\".\nHistory\nAboriginal peoples have lived in the Outback for at least 50,000 years and occupied all Outback regions, including the driest deserts, when Europeans first entered central Australia in the 1800s. Many Aboriginal Australians retain strong physical and cultural links to their traditional country and are legally recognised as the Traditional Owners of large parts of the Outback under Commonwealth Native Title legislation."]], "0_corpus": "wikipedia", "1_conv_id": "dbd91289282c48ce8a535ca08053bf2f", "1_model_name": "text-embedding-3-large", "1_prompt": "the vast interior rural area of australia is known as the", "1_output": [["the vast interior rural area of australia is known as the", "Outback\n\nThe Outback is a remote, vast, sparsely populated area of Australia. The Outback is more remote than the bush. While often envisaged as being arid, the Outback regions extend from the northern to southern Australian coastlines and encompass a number of climatic zones, including tropical and monsoonal climates in northern areas, arid areas in the \"red centre\" and semi-arid and temperate climates in southerly regions. The total population is estimated at 607,000 people.\nGeographically, the Outback is unified by a combination of factors, most notably a low human population density, a largely intact natural environment and, in many places, low-intensity land uses, such as pastoralism (livestock grazing) in which production is reliant on the natural environment. The Outback is deeply ingrained in Australian heritage, history and folklore. In Australian art the subject of the Outback has been vogue, particularly in the 1940s. In 2009, as part of the Q150 celebrations, the Queensland Outback was announced as one of the Q150 Icons of Queensland for its role as a \"natural attraction\".\nHistory\nAboriginal peoples have lived in the Outback for at least 50,000 years and occupied all Outback regions, including the driest deserts, when Europeans first entered central Australia in the 1800s. Many Aboriginal Australians retain strong physical and cultural links to their traditional country and are legally recognised as the Traditional Owners of large parts of the Outback under Commonwealth Native Title legislation."]], "1_corpus": "wikipedia"}
+{"tstamp": 1722545337.2965, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "47edbe2046874c01b87fa9d07ba0eab9", "0_model_name": "Salesforce/SFR-Embedding-2_R", "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": "194fc9b4aa6e48f89658f98780eb2551", "1_model_name": "BM25", "1_prompt": "Which test was devised to determine whether robots can think?", "1_output": [["Which test was devised to determine whether robots can think?", "Artificial intelligence\n\nIn the late teens and early 2020s, AGI companies began to deliver programs that created enormous interest. In 2015, AlphaGo, developed by DeepMind, beat the world champion Go player. The program was taught only the rules of the game and developed strategy by itself. GPT-3 is a large language model that was released in 2020 by OpenAI and is capable of generating high-quality human-like text. These programs, and others, inspired an aggressive AI boom, where large companies began investing billions in AI research. According to AI Impacts, about $50 billion annually was invested in \"AI\" around 2022 in the U.S. alone and about 20% of the new U.S. Computer Science PhD graduates have specialized in \"AI\".\nAbout 800,000 \"AI\"-related U.S. job openings existed in 2022.\nPhilosophy\nDefining artificial intelligence\nAlan Turing wrote in 1950 \"I propose to consider the question 'can machines think'?\" He advised changing the question from whether a machine \"thinks\", to \"whether or not it is possible for machinery to show intelligent behaviour\". He devised the Turing test, which measures the ability of a machine to simulate human conversation. Since we can only observe the behavior of the machine, it does not matter if it is \"actually\" thinking or literally has a \"mind\". Turing notes that we can not determine these things about other people but \"it is usual to have a polite convention that everyone thinks.\""]], "1_corpus": "wikipedia"}
+{"tstamp": 1722545338.45, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "621c1326eeab4875aa238e67dfb13464", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "when does it's a wonderful life comes on", "0_output": [["when does it's a wonderful life comes on", "It's a Wonderful Life\n\nOn May 26, 1947, the Federal Bureau of Investigation issued a memo stating, \"With regard to the picture It's a Wonderful Life, [redacted] stated in substance that the film represented rather obvious attempts to discredit bankers by casting Lionel Barrymore as a 'scrooge-type' so that he would be the most hated man in the picture. This, according to these sources, is a common trick used by Communists. [In] addition, [redacted] stated that, in his opinion, this picture deliberately maligned the upper class, attempting to show the people who had money were mean and despicable characters.\" Film historian Andrew Sarris observed as \"curious\" that \"the censors never noticed that the villainous Mr. Potter gets away with robbery without being caught or punished in any way\".\nIn 1990, It's a Wonderful Life was deemed \"culturally, historically, or aesthetically significant\" by the United States Library of Congress and selected for preservation in their National Film Registry.\nIn 2002, Channel 4 in the United Kingdom ranked It's a Wonderful Life as the seventh-greatest film ever made in its poll \"The 100 Greatest Films\". The channel airs the film to British viewers annually on Christmas Eve.\nIn June 2008, AFI revealed its 10 Top 10, the best 10 films in 10 \"classic\" American film genres, after polling over 1,500 people from the creative community. It's a Wonderful Life was acknowledged as the third-best film in the fantasy genre."]], "0_corpus": "wikipedia", "1_conv_id": "89ef42365c4142d08da2405ab7e90d39", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "when does it's a wonderful life comes on", "1_output": [["when does it's a wonderful life comes on", "It's a Wonderful Life\n\nAnother musical stage adaptation of the film, titled It's a Wonderful Life – The Musical, was written by Bruce Greer and Keith Ferguson. This version premiered at the Majestic Theatre, Dallas, Texas, in 1998. It was an annual Christmas show at the theater for five years. It has since been performed at venues all around the United States.\nIn July 2019, it was revealed that another stage musical adaptation was in development with music and lyrics by Paul McCartney (making his musical theatre debut), lyrics and book by Lee Hall and produced by Bill Kenwright. The musical was set to debut late 2020, but is still yet to be announced.\nA 1986 skit on Saturday Night Live features William Shatner introducing the \"lost ending\" of It's a Wonderful Life in which George Bailey (Dana Carvey) and the citizens of Bedford Falls discover that Mr. Potter (Jon Lovitz) has stolen George's money and take turns beating him up in his office.\nIn 1992, the final episode of Tiny Toon Adventures parodied It's A Wonderful Life entitled \"It's A Wonderful Tiny Toon Christmas\". In it, Buster Bunny feels sad after the failure of his play and wishes he had never become a Tiny Toon, so a guardian angel shows Buster what life would have been like without him."]], "1_corpus": "wikipedia"}
+{"tstamp": 1722545359.7608, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "a2448e21fa044f958450cb670f28eaa4", "0_model_name": "BM25", "0_prompt": "Paper proposing end-to-end dialogue state tracking using BERT for scalable and efficient slot value identification.", "0_output": [["Paper proposing end-to-end dialogue state tracking using BERT for scalable and efficient slot value identification.", "Title: BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformer\n\nAbstract: An important yet rarely tackled problem in dialogue state tracking (DST) is scalability for dynamic ontology (e.g., movie, restaurant) and unseen slot values. We focus on a specific condition, where the ontology is unknown to the state tracker, but the target slot value (except for none and dontcare), possibly unseen during training, can be found as word segment in the dialogue context. Prior approaches often rely on candidate generation from n-gram enumeration or slot tagger outputs, which can be inefficient or suffer from error propagation. We propose BERT-DST, an end-to-end dialogue state tracker which directly extracts slot values from the dialogue context. We use BERT as dialogue context encoder whose contextualized language representations are suitable for scalable DST to identify slot values from their semantic context. Furthermore, we employ encoder parameter sharing across all slots with two advantages: (1) Number of parameters does not grow linearly with the ontology. (2) Language representation knowledge can be transferred among slots. Empirical evaluation shows BERT-DST with cross-slot parameter sharing outperforms prior work on the benchmark scalable DST datasets Sim-M and Sim-R, and achieves competitive performance on the standard DSTC2 and WOZ 2.0 datasets."]], "0_corpus": "arxiv", "1_conv_id": "73631749430c455091032c3a73321718", "1_model_name": "GritLM/GritLM-7B", "1_prompt": "Paper proposing end-to-end dialogue state tracking using BERT for scalable and efficient slot value identification.", "1_output": [["Paper proposing end-to-end dialogue state tracking using BERT for scalable and efficient slot value identification.", "Title: BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformer\n\nAbstract: An important yet rarely tackled problem in dialogue state tracking (DST) is scalability for dynamic ontology (e.g., movie, restaurant) and unseen slot values. We focus on a specific condition, where the ontology is unknown to the state tracker, but the target slot value (except for none and dontcare), possibly unseen during training, can be found as word segment in the dialogue context. Prior approaches often rely on candidate generation from n-gram enumeration or slot tagger outputs, which can be inefficient or suffer from error propagation. We propose BERT-DST, an end-to-end dialogue state tracker which directly extracts slot values from the dialogue context. We use BERT as dialogue context encoder whose contextualized language representations are suitable for scalable DST to identify slot values from their semantic context. Furthermore, we employ encoder parameter sharing across all slots with two advantages: (1) Number of parameters does not grow linearly with the ontology. (2) Language representation knowledge can be transferred among slots. Empirical evaluation shows BERT-DST with cross-slot parameter sharing outperforms prior work on the benchmark scalable DST datasets Sim-M and Sim-R, and achieves competitive performance on the standard DSTC2 and WOZ 2.0 datasets."]], "1_corpus": "arxiv"}

data/retrieval_individual-928d45d9-19ab-4d05-9aae-4b6574886d0d.jsonl

@@ -61,3 +61,9 @@
 {"tstamp": 1722545218.9133, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722545218.1939, "finish": 1722545218.9133, "ip": "", "conv_id": "dbd91289282c48ce8a535ca08053bf2f", "model_name": "text-embedding-3-large", "prompt": "the vast interior rural area of australia is known as the", "output": [["the vast interior rural area of australia is known as the", "Outback\n\nThe Outback is a remote, vast, sparsely populated area of Australia. The Outback is more remote than the bush. While often envisaged as being arid, the Outback regions extend from the northern to southern Australian coastlines and encompass a number of climatic zones, including tropical and monsoonal climates in northern areas, arid areas in the \"red centre\" and semi-arid and temperate climates in southerly regions. The total population is estimated at 607,000 people.\nGeographically, the Outback is unified by a combination of factors, most notably a low human population density, a largely intact natural environment and, in many places, low-intensity land uses, such as pastoralism (livestock grazing) in which production is reliant on the natural environment. The Outback is deeply ingrained in Australian heritage, history and folklore. In Australian art the subject of the Outback has been vogue, particularly in the 1940s. In 2009, as part of the Q150 celebrations, the Queensland Outback was announced as one of the Q150 Icons of Queensland for its role as a \"natural attraction\".\nHistory\nAboriginal peoples have lived in the Outback for at least 50,000 years and occupied all Outback regions, including the driest deserts, when Europeans first entered central Australia in the 1800s. Many Aboriginal Australians retain strong physical and cultural links to their traditional country and are legally recognised as the Traditional Owners of large parts of the Outback under Commonwealth Native Title legislation."]], "corpus": "wikipedia"}
 {"tstamp": 1722545254.1039, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722545251.7556, "finish": 1722545254.1039, "ip": "", "conv_id": "621c1326eeab4875aa238e67dfb13464", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "when does it's a wonderful life comes on", "output": [["when does it's a wonderful life comes on", "It's a Wonderful Life\n\nOn May 26, 1947, the Federal Bureau of Investigation issued a memo stating, \"With regard to the picture It's a Wonderful Life, [redacted] stated in substance that the film represented rather obvious attempts to discredit bankers by casting Lionel Barrymore as a 'scrooge-type' so that he would be the most hated man in the picture. This, according to these sources, is a common trick used by Communists. [In] addition, [redacted] stated that, in his opinion, this picture deliberately maligned the upper class, attempting to show the people who had money were mean and despicable characters.\" Film historian Andrew Sarris observed as \"curious\" that \"the censors never noticed that the villainous Mr. Potter gets away with robbery without being caught or punished in any way\".\nIn 1990, It's a Wonderful Life was deemed \"culturally, historically, or aesthetically significant\" by the United States Library of Congress and selected for preservation in their National Film Registry.\nIn 2002, Channel 4 in the United Kingdom ranked It's a Wonderful Life as the seventh-greatest film ever made in its poll \"The 100 Greatest Films\". The channel airs the film to British viewers annually on Christmas Eve.\nIn June 2008, AFI revealed its 10 Top 10, the best 10 films in 10 \"classic\" American film genres, after polling over 1,500 people from the creative community. It's a Wonderful Life was acknowledged as the third-best film in the fantasy genre."]], "corpus": "wikipedia"}
 {"tstamp": 1722545254.1039, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1722545251.7556, "finish": 1722545254.1039, "ip": "", "conv_id": "89ef42365c4142d08da2405ab7e90d39", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "when does it's a wonderful life comes on", "output": [["when does it's a wonderful life comes on", "It's a Wonderful Life\n\nAnother musical stage adaptation of the film, titled It's a Wonderful Life – The Musical, was written by Bruce Greer and Keith Ferguson. This version premiered at the Majestic Theatre, Dallas, Texas, in 1998. It was an annual Christmas show at the theater for five years. It has since been performed at venues all around the United States.\nIn July 2019, it was revealed that another stage musical adaptation was in development with music and lyrics by Paul McCartney (making his musical theatre debut), lyrics and book by Lee Hall and produced by Bill Kenwright. The musical was set to debut late 2020, but is still yet to be announced.\nA 1986 skit on Saturday Night Live features William Shatner introducing the \"lost ending\" of It's a Wonderful Life in which George Bailey (Dana Carvey) and the citizens of Bedford Falls discover that Mr. Potter (Jon Lovitz) has stolen George's money and take turns beating him up in his office.\nIn 1992, the final episode of Tiny Toon Adventures parodied It's A Wonderful Life entitled \"It's A Wonderful Tiny Toon Christmas\". In it, Buster Bunny feels sad after the failure of his play and wishes he had never become a Tiny Toon, so a guardian angel shows Buster what life would have been like without him."]], "corpus": "wikipedia"}
+{"tstamp": 1722545316.3511, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722545316.0106, "finish": 1722545316.3511, "ip": "", "conv_id": "47edbe2046874c01b87fa9d07ba0eab9", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "corpus": "wikipedia"}
+{"tstamp": 1722545316.3511, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722545316.0106, "finish": 1722545316.3511, "ip": "", "conv_id": "194fc9b4aa6e48f89658f98780eb2551", "model_name": "BM25", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Artificial intelligence\n\nIn the late teens and early 2020s, AGI companies began to deliver programs that created enormous interest. In 2015, AlphaGo, developed by DeepMind, beat the world champion Go player. The program was taught only the rules of the game and developed strategy by itself. GPT-3 is a large language model that was released in 2020 by OpenAI and is capable of generating high-quality human-like text. These programs, and others, inspired an aggressive AI boom, where large companies began investing billions in AI research. According to AI Impacts, about $50 billion annually was invested in \"AI\" around 2022 in the U.S. alone and about 20% of the new U.S. Computer Science PhD graduates have specialized in \"AI\".\nAbout 800,000 \"AI\"-related U.S. job openings existed in 2022.\nPhilosophy\nDefining artificial intelligence\nAlan Turing wrote in 1950 \"I propose to consider the question 'can machines think'?\" He advised changing the question from whether a machine \"thinks\", to \"whether or not it is possible for machinery to show intelligent behaviour\". He devised the Turing test, which measures the ability of a machine to simulate human conversation. Since we can only observe the behavior of the machine, it does not matter if it is \"actually\" thinking or literally has a \"mind\". Turing notes that we can not determine these things about other people but \"it is usual to have a polite convention that everyone thinks.\""]], "corpus": "wikipedia"}
+{"tstamp": 1722545349.0792, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722545348.5222, "finish": 1722545349.0792, "ip": "", "conv_id": "a2448e21fa044f958450cb670f28eaa4", "model_name": "BM25", "prompt": "Paper proposing end-to-end dialogue state tracking using BERT for scalable and efficient slot value identification.", "output": [["Paper proposing end-to-end dialogue state tracking using BERT for scalable and efficient slot value identification.", "Title: BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformer\n\nAbstract: An important yet rarely tackled problem in dialogue state tracking (DST) is scalability for dynamic ontology (e.g., movie, restaurant) and unseen slot values. We focus on a specific condition, where the ontology is unknown to the state tracker, but the target slot value (except for none and dontcare), possibly unseen during training, can be found as word segment in the dialogue context. Prior approaches often rely on candidate generation from n-gram enumeration or slot tagger outputs, which can be inefficient or suffer from error propagation. We propose BERT-DST, an end-to-end dialogue state tracker which directly extracts slot values from the dialogue context. We use BERT as dialogue context encoder whose contextualized language representations are suitable for scalable DST to identify slot values from their semantic context. Furthermore, we employ encoder parameter sharing across all slots with two advantages: (1) Number of parameters does not grow linearly with the ontology. (2) Language representation knowledge can be transferred among slots. Empirical evaluation shows BERT-DST with cross-slot parameter sharing outperforms prior work on the benchmark scalable DST datasets Sim-M and Sim-R, and achieves competitive performance on the standard DSTC2 and WOZ 2.0 datasets."]], "corpus": "arxiv"}
+{"tstamp": 1722545349.0792, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722545348.5222, "finish": 1722545349.0792, "ip": "", "conv_id": "73631749430c455091032c3a73321718", "model_name": "GritLM/GritLM-7B", "prompt": "Paper proposing end-to-end dialogue state tracking using BERT for scalable and efficient slot value identification.", "output": [["Paper proposing end-to-end dialogue state tracking using BERT for scalable and efficient slot value identification.", "Title: BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformer\n\nAbstract: An important yet rarely tackled problem in dialogue state tracking (DST) is scalability for dynamic ontology (e.g., movie, restaurant) and unseen slot values. We focus on a specific condition, where the ontology is unknown to the state tracker, but the target slot value (except for none and dontcare), possibly unseen during training, can be found as word segment in the dialogue context. Prior approaches often rely on candidate generation from n-gram enumeration or slot tagger outputs, which can be inefficient or suffer from error propagation. We propose BERT-DST, an end-to-end dialogue state tracker which directly extracts slot values from the dialogue context. We use BERT as dialogue context encoder whose contextualized language representations are suitable for scalable DST to identify slot values from their semantic context. Furthermore, we employ encoder parameter sharing across all slots with two advantages: (1) Number of parameters does not grow linearly with the ontology. (2) Language representation knowledge can be transferred among slots. Empirical evaluation shows BERT-DST with cross-slot parameter sharing outperforms prior work on the benchmark scalable DST datasets Sim-M and Sim-R, and achieves competitive performance on the standard DSTC2 and WOZ 2.0 datasets."]], "corpus": "arxiv"}
+{"tstamp": 1722545369.2006, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722545369.0737, "finish": 1722545369.2006, "ip": "", "conv_id": "80a257794852495a9df012347dcf555c", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "Paper analyzing modeling decisions for large language models with a limited GPU budget", "output": [["Paper analyzing modeling decisions for large language models with a limited GPU budget", "Title: Slow Manifolds for Infinite-Dimensional Evolution Equations\n\nAbstract: We extend classical finite-dimensional Fenichel theory in two directions to infinite dimensions. Under comparably weak assumptions we show that the solution of an infinite-dimensional fast-slow system is approximated well by the corresponding slow flow. After that we construct a two-parameter family of slow manifolds $S_{\\epsilon,\\zeta}$ under more restrictive assumptions on the linear part of the slow equation. The second parameter $\\zeta$ does not appear in the finite-dimensional setting and describes a certain splitting of the slow variable space in a fast decaying part and its complement. The finite-dimensional setting is contained as a special case in which $S_{\\epsilon,\\zeta}$ does not depend on $\\zeta$. Finally, we apply our new techniques to three examples of fast-slow systems of partial differential equations."]], "corpus": "arxiv"}
+{"tstamp": 1722545369.2006, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722545369.0737, "finish": 1722545369.2006, "ip": "", "conv_id": "15b5b5b444ae49a293873393cde587ec", "model_name": "GritLM/GritLM-7B", "prompt": "Paper analyzing modeling decisions for large language models with a limited GPU budget", "output": [["Paper analyzing modeling decisions for large language models with a limited GPU budget", "Title: What Language Model to Train if You Have One Million GPU Hours?\n\nAbstract: The crystallization of modeling methods around the Transformer architecture has been a boon for practitioners. Simple, well-motivated architectural variations can transfer across tasks and scale, increasing the impact of modeling research. However, with the emergence of state-of-the-art 100B+ parameters models, large language models are increasingly expensive to accurately design and train. Notably, it can be difficult to evaluate how modeling decisions may impact emergent capabilities, given that these capabilities arise mainly from sheer scale alone. In the process of building BLOOM--the Big Science Large Open-science Open-access Multilingual language model--our goal is to identify an architecture and training setup that makes the best use of our 1,000,000 A100-GPU-hours budget. Specifically, we perform an ablation study at the billion-parameter scale comparing different modeling practices and their impact on zero-shot generalization. In addition, we study the impact of various popular pre-training corpora on zero-shot generalization. We also study the performance of a multilingual model and how it compares to the English-only one. Finally, we consider the scaling behaviour of Transformers to choose the target model size, shape, and training setup. All our models and code are open-sourced at https://huggingface.co/bigscience ."]], "corpus": "arxiv"}