Scheduled Commit

data/retrieval_battle-839a5845-da8b-404c-8647-ecbee5eb2a3c.jsonl

@@ -2,3 +2,4 @@
 {"tstamp": 1725313131.3803, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "9015c230a93b4f98b500337e5d50de29", "0_model_name": "Salesforce/SFR-Embedding-2_R", "0_prompt": "A paper discussing a biomedical generative language model that adapts BART and achieves state-of-the-art performance.", "0_output": [["A paper discussing a biomedical generative language model that adapts BART and achieves state-of-the-art performance.", "Title: BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model\n\nAbstract: Pretrained language models have served as important backbones for natural language processing. Recently, in-domain pretraining has been shown to benefit various domain-specific downstream tasks. In the biomedical domain, natural language generation (NLG) tasks are of critical importance, while understudied. Approaching natural language understanding (NLU) tasks as NLG achieves satisfying performance in the general domain through constrained language generation or language prompting. We emphasize the lack of in-domain generative language models and the unsystematic generative downstream benchmarks in the biomedical domain, hindering the development of the research community. In this work, we introduce the generative language model BioBART that adapts BART to the biomedical domain. We collate various biomedical language generation tasks including dialogue, summarization, entity linking, and named entity recognition. BioBART pretrained on PubMed abstracts has enhanced performance compared to BART and set strong baselines on several tasks. Furthermore, we conduct ablation studies on the pretraining tasks for BioBART and find that sentence permutation has negative effects on downstream tasks."]], "0_corpus": "arxiv", "1_conv_id": "f258adc61453498b9b380b6ae8738f98", "1_model_name": "voyage-multilingual-2", "1_prompt": "A paper discussing a biomedical generative language model that adapts BART and achieves state-of-the-art performance.", "1_output": [["A paper discussing a biomedical generative language model that adapts BART and achieves state-of-the-art performance.", "Title: BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model\n\nAbstract: Pretrained language models have served as important backbones for natural language processing. Recently, in-domain pretraining has been shown to benefit various domain-specific downstream tasks. In the biomedical domain, natural language generation (NLG) tasks are of critical importance, while understudied. Approaching natural language understanding (NLU) tasks as NLG achieves satisfying performance in the general domain through constrained language generation or language prompting. We emphasize the lack of in-domain generative language models and the unsystematic generative downstream benchmarks in the biomedical domain, hindering the development of the research community. In this work, we introduce the generative language model BioBART that adapts BART to the biomedical domain. We collate various biomedical language generation tasks including dialogue, summarization, entity linking, and named entity recognition. BioBART pretrained on PubMed abstracts has enhanced performance compared to BART and set strong baselines on several tasks. Furthermore, we conduct ablation studies on the pretraining tasks for BioBART and find that sentence permutation has negative effects on downstream tasks."]], "1_corpus": "arxiv"}
 {"tstamp": 1725349822.5522, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "7ed7043857994c968603dd332db4ecf6", "0_model_name": "voyage-multilingual-2", "0_prompt": "Seeking a paper on refining model predictions layer-by-layer in transformers with improved techniques.", "0_output": [["Seeking a paper on refining model predictions layer-by-layer in transformers with improved techniques.", "Title: Not all layers are equally as important: Every Layer Counts BERT\n\nAbstract: This paper introduces a novel modification of the transformer architecture, tailored for the data-efficient pretraining of language models. This aspect is evaluated by participating in the BabyLM challenge, where our solution won both the strict and strict-small tracks. Our approach allows each transformer layer to select which outputs of previous layers to process. The empirical results verify the potential of this simple modification and show that not all layers are equally as important."]], "0_corpus": "arxiv", "1_conv_id": "01d88ab76b284a41a13c72b7b04e7ac0", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_prompt": "Seeking a paper on refining model predictions layer-by-layer in transformers with improved techniques.", "1_output": [["Seeking a paper on refining model predictions layer-by-layer in transformers with improved techniques.", "Title: Simple and Superlattice Turing Patterns in Reaction-Diffusion Systems: Bifurcation, Bistability, and Parameter Collapse\n\nAbstract: This paper investigates the competition between both simple (e.g. stripes, hexagons) and ``superlattice'' (super squares, super hexagons) Turing patterns in two-component reaction-diffusion systems. ``Superlattice'' patterns are formed from eight or twelve Fourier modes, and feature structure at two different length scales. Using perturbation theory, we derive simple analytical expressions for the bifurcation equation coefficients on both rhombic and hexagonal lattices. These expressions show that, no matter how complicated the reaction kinectics, the nonlinear reaction terms reduce to just four effective terms within the bifurcation equation coefficients. Moreover, at the hexagonal degeneracy -- when the quadratic term in the hexagonal bifurcation equation disappears -- the number of effective system parameters drops to two, allowing a complete characterization of the possible bifurcation results at this degeneracy. The general results are then applied to specific model equations, to investigate the stability of different patterns within those models."]], "1_corpus": "arxiv"}
 {"tstamp": 1725353430.4731, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "47ce4b9a08d146f1af9f1da09f9f39ff", "0_model_name": "text-embedding-3-large", "0_prompt": "Hello darkness my old friend.", "0_output": [["Hello darkness my old friend.", "Darkness\n\nDarkness is defined as a lack of illumination, an absence of visible light, or a surface that absorbs light, such as a black one.\nHuman vision is unable to distinguish colors in conditions of very low luminance because the hue-sensitive photoreceptor cells on the retina are inactive when light levels are insufficient, in the range of visual perception referred to as scotopic vision.\nThe emotional response to darkness has generated metaphorical usages of the term in many cultures, often used to describe an unhappy or foreboding feeling.\n\"Darkness\" may also refer to night, which occurs when the Sun is more than 18° below the horizon.\nScientific\nPerception\nThe perception of darkness differs from the mere absence of light due to the effects of after images on perception. In perceiving, the eye is active, and the part of the retina that is unstimulated produces a complementary afterimage."]], "0_corpus": "wikipedia", "1_conv_id": "b1fbb12d1d984d969c52b3a58a49ad16", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": "Hello darkness my old friend.", "1_output": [["Hello darkness my old friend.", "Darkness\n\nDarkness is defined as a lack of illumination, an absence of visible light, or a surface that absorbs light, such as a black one.\nHuman vision is unable to distinguish colors in conditions of very low luminance because the hue-sensitive photoreceptor cells on the retina are inactive when light levels are insufficient, in the range of visual perception referred to as scotopic vision.\nThe emotional response to darkness has generated metaphorical usages of the term in many cultures, often used to describe an unhappy or foreboding feeling.\n\"Darkness\" may also refer to night, which occurs when the Sun is more than 18° below the horizon.\nScientific\nPerception\nThe perception of darkness differs from the mere absence of light due to the effects of after images on perception. In perceiving, the eye is active, and the part of the retina that is unstimulated produces a complementary afterimage."]], "1_corpus": "wikipedia"}
+{"tstamp": 1725359933.4439, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "73d20eeed3434cd684566eb1c112fdc5", "0_model_name": "voyage-multilingual-2", "0_prompt": "phycology ", "0_output": [["phycology ", "Phycology\n\nAlgae are important as primary producers in aquatic ecosystems. Most algae are eukaryotic, photosynthetic organisms that live in a wet environment. They are distinguished from the higher plants by a lack of true roots, stems or leaves. They do not produce flowers. Many species are single-celled and microscopic (including phytoplankton and other microalgae); many others are multicellular to one degree or another, some of these growing to large size (for example, seaweeds such as kelp and Sargassum).\nPhycology includes the study of prokaryotic forms known as blue-green algae or cyanobacteria. A number of microscopic algae also occur as symbionts in lichens.\nPhycologists typically focus on either freshwater or ocean algae, and further within those areas, either diatoms or soft algae.\nHistory of phycology\nWhile both the ancient Greeks and Romans knew of algae, and the ancient Chinese even cultivated certain varieties as food, the scientific study of algae began in the late 18th century with the description and naming of Fucus maximus (now Ecklonia maxima) in 1757 by Pehr Osbeck. This was followed by the descriptive work of scholars such as Dawson Turner and Carl Adolph Agardh, but it was not until later in the 19th century that efforts were made by J.V. Lamouroux and William Henry Harvey to create significant groupings within the algae. Harvey has been called \"the father of modern phycology\" in part for his division of the algae into four major divisions based upon their pigmentation."]], "0_corpus": "wikipedia", "1_conv_id": "72b904eb36b941188eae16186c30a3a3", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "phycology ", "1_output": [["phycology ", "Phycology\n\nPhycology () is the scientific study of algae.  Also known as algology, phycology is a branch of life science."]], "1_corpus": "wikipedia"}

data/retrieval_individual-839a5845-da8b-404c-8647-ecbee5eb2a3c.jsonl

@@ -103,3 +103,7 @@
 {"tstamp": 1725353410.0275, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1725353409.5379, "finish": 1725353410.0275, "ip": "", "conv_id": "b1fbb12d1d984d969c52b3a58a49ad16", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "Hello darkness my old friend.", "output": [["Hello darkness my old friend.", "Darkness\n\nDarkness is defined as a lack of illumination, an absence of visible light, or a surface that absorbs light, such as a black one.\nHuman vision is unable to distinguish colors in conditions of very low luminance because the hue-sensitive photoreceptor cells on the retina are inactive when light levels are insufficient, in the range of visual perception referred to as scotopic vision.\nThe emotional response to darkness has generated metaphorical usages of the term in many cultures, often used to describe an unhappy or foreboding feeling.\n\"Darkness\" may also refer to night, which occurs when the Sun is more than 18° below the horizon.\nScientific\nPerception\nThe perception of darkness differs from the mere absence of light due to the effects of after images on perception. In perceiving, the eye is active, and the part of the retina that is unstimulated produces a complementary afterimage."]], "corpus": "wikipedia"}
 {"tstamp": 1725353454.2333, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1725353453.1182, "finish": 1725353454.2333, "ip": "", "conv_id": "896c26b1975c43c7b25d600c088bc7fc", "model_name": "voyage-multilingual-2", "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 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"]], "corpus": "wikipedia"}
 {"tstamp": 1725353454.2333, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1725353453.1182, "finish": 1725353454.2333, "ip": "", "conv_id": "69a255e691474b6791275b0d9db3796e", "model_name": "text-embedding-004", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "corpus": "wikipedia"}
+{"tstamp": 1725359918.8132, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1725359918.3679, "finish": 1725359918.8132, "ip": "", "conv_id": "73d20eeed3434cd684566eb1c112fdc5", "model_name": "voyage-multilingual-2", "prompt": "phycology ", "output": [["phycology ", "Phycology\n\nAlgae are important as primary producers in aquatic ecosystems. Most algae are eukaryotic, photosynthetic organisms that live in a wet environment. They are distinguished from the higher plants by a lack of true roots, stems or leaves. They do not produce flowers. Many species are single-celled and microscopic (including phytoplankton and other microalgae); many others are multicellular to one degree or another, some of these growing to large size (for example, seaweeds such as kelp and Sargassum).\nPhycology includes the study of prokaryotic forms known as blue-green algae or cyanobacteria. A number of microscopic algae also occur as symbionts in lichens.\nPhycologists typically focus on either freshwater or ocean algae, and further within those areas, either diatoms or soft algae.\nHistory of phycology\nWhile both the ancient Greeks and Romans knew of algae, and the ancient Chinese even cultivated certain varieties as food, the scientific study of algae began in the late 18th century with the description and naming of Fucus maximus (now Ecklonia maxima) in 1757 by Pehr Osbeck. This was followed by the descriptive work of scholars such as Dawson Turner and Carl Adolph Agardh, but it was not until later in the 19th century that efforts were made by J.V. Lamouroux and William Henry Harvey to create significant groupings within the algae. Harvey has been called \"the father of modern phycology\" in part for his division of the algae into four major divisions based upon their pigmentation."]], "corpus": "wikipedia"}
+{"tstamp": 1725359918.8132, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1725359918.3679, "finish": 1725359918.8132, "ip": "", "conv_id": "72b904eb36b941188eae16186c30a3a3", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "phycology ", "output": [["phycology ", "Phycology\n\nPhycology () is the scientific study of algae.  Also known as algology, phycology is a branch of life science."]], "corpus": "wikipedia"}
+{"tstamp": 1725359955.0713, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1725359954.8888, "finish": 1725359955.0713, "ip": "", "conv_id": "f367d52e80574b7f9083087c811dd47c", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "mental health ", "output": [["mental health ", "Mental health\n\nEvidence from the WHO suggests that nearly half of the world's population is affected by mental illness with an impact on their self-esteem, relationships and ability to function in everyday life. An individual's emotional health can impact their physical health. Poor mental health can lead to problems such as the inability to make adequate decisions and substance use disorders.\nGood mental health can improve life quality whereas poor mental health can worsen it. According to Richards, Campania, & Muse-Burke, \"There is growing evidence that is showing emotional abilities are associated with pro-social behaviors such as stress management and physical health.\" Their research also concluded that people who lack emotional expression are inclined to anti-social behaviors (e.g., substance use disorder and alcohol use disorder, physical fights, vandalism), which reflects one's mental health and suppressed emotions. Adults and children who face mental illness may experience social stigma, which can exacerbate the issues.\nGlobal prevalence\nMental health can be seen as a continuum, where an individual's mental health may have many different possible values. Mental wellness is viewed as a positive attribute; this definition of mental health highlights emotional well-being, the capacity to live a full and creative life, and the flexibility to deal with life's inevitable challenges. Some discussions are formulated in terms of contentment or happiness. Many therapeutic systems and self-help books offer methods and philosophies espousing strategies and techniques vaunted as effective for further improving the mental wellness. Positive psychology is increasingly prominent in mental health."]], "corpus": "wikipedia"}
+{"tstamp": 1725359955.0713, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1725359954.8888, "finish": 1725359955.0713, "ip": "", "conv_id": "e9c26f954a3445a49b6cd4deee5663bb", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "mental health ", "output": [["mental health ", "Mental health\n\nMental disorders Mental health, as defined by the Public Health Agency of Canada, is an individual's capacity to feel, think, and act in ways to achieve a better quality of life while respecting personal, social, and cultural boundaries. Impairment of any of these are risk factor for mental disorders, or mental illnesses, which are a component of mental health. In 2019, about 970 million people worldwide suffered from a mental disorder, with anxiety and depression being the most common. The number of people suffering from mental disorders has risen significantly throughout the years. Mental disorders are defined as health conditions that affect and alter cognitive functioning, emotional responses, and behavior associated with distress and/or impaired functioning. The ICD-11 is the global standard used to diagnose, treat, research, and report various mental disorders. In the United States, the DSM-5 is used as the classification system of mental disorders.\nMental health is associated with a number of lifestyle factors such as diet, exercise, stress, drug abuse, social connections and interactions. Psychiatrists, psychologists, licensed professional clinical counselors, social workers, nurse practitioners, and family physicians can help manage mental illness with treatments such as therapy, counseling, and medication.\nHistory\nEarly history\nIn the mid-19th century, William Sweetser was the first to coin the term mental hygiene, which can be seen as the precursor to contemporary approaches to work on promoting positive mental health. Isaac Ray, the fourth president of the American Psychiatric Association and one of its founders, further defined mental hygiene as \"the art of preserving the mind against all incidents and influences calculated to deteriorate its qualities, impair its energies, or derange its movements\"."]], "corpus": "wikipedia"}