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{: 1722304978.8845, : , : , : , : {}, : 1722304977.8763, : 1722304978.8845, : , : , : , : , : [[, Mask and Infill\Mask and Infill\]], : } |
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{: 1722304978.8845, : , : , : , : {}, : 1722304977.8763, : 1722304978.8845, : , : , : , : , : [[, ]], : } |
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{: 1722304986.0474, : , : , : , : {}, : 1722304985.1247, : 1722304986.0474, : , : , : , : , : [[, ]], : } |
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{: 1722304986.0474, : , : , : , : {}, : 1722304985.1247, : 1722304986.0474, : , : , : , : , : [[, Baby, It's Cold Outside\" is a popular song written by Frank Loesser in 1944 and popularized in the 1949 film Neptune's Daughter. While the lyrics make no mention of a holiday, it is commonly regarded as a Christmas song owing to its winter theme. The song was released in eight recordings in 1949—including well-known versions by Ella Fitzgerald and Louis Jordan, and by Dean Martin and Marilyn Maxwell—and has been covered numerous times since.\nHistory\nIn 1944, Loesser wrote \ to sing with his wife, Lynn Garland, at their housewarming party in New York City at the Navarro Hotel. They sang the song to indicate to guests that it was time to leave. Garland has written that after the first performance, \ In 1948, after years of performing the song, Loesser sold it to MGM for the 1949 romantic comedy Neptune's Daughter. Garland was furious: \"I felt as betrayed as if I'd caught him in bed with another woman.\]], : } |
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{: 1722304990.98, : , : , : , : {}, : 1722304990.7411, : 1722304990.98, : , : , : , : , : [[, ]], : } |
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{: 1722304990.98, : , : , : , : {}, : 1722304990.7411, : 1722304990.98, : , : , : , : , : [[, ]], : } |
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{: 1722305000.0153, : , : , : , : {}, : 1722304999.8039, : 1722305000.0153, : , : , : , : , : [[, ]], : } |
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{: 1722305000.0153, : , : , : , : {}, : 1722304999.8039, : 1722305000.0153, : , : , : , : , : [[, ]], : } |
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{: 1722305078.6563, : , : , : , : {}, : 1722305078.4572, : 1722305078.6563, : , : , : , : , : [[, Computing Machinery and Intelligence\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: \ 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 \.\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 \, \, or \, 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"} |
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{"tstamp": 1722305178.7998, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722305178.5602, "finish": 1722305178.7998, "ip": "", "conv_id": "a721ba1ec728453388b30d0efed8f994", "model_name": "GritLM/GritLM-7B", "prompt": "what is the difference between an electronic signal and an electromagnetic signal?", "output": [["what is the difference between an electronic signal and an electromagnetic signal?", "Gaming Stackexchange\n\nQ: What's the difference between semaphore and electric signals? In OpenTTD you can use electric signals and semaphore signals. In every instance I've ever seen people use electric signals to direct their trains. On the wiki the only mention of semaphores is \"don't hold down control otherwise you'll end up with a semaphore\". This suggests to me that semaphores are unwanted and have a different function to normal signals that is only applicable in complex circumstances. I couldn't find anything on the internet detailing their function. \nWhat is the functional difference between semaphore and electric signals?\n\nA: There is no functional difference between the two. By default the game will construct semaphores before a certain date defined in the Advanced Game Options > Construction section, to mimic the introduction of electrical signals in real life. Holding down Ctrl overrides that and builds semaphores instead. \ncorpusstackexchange |
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tstamptask_typeretrievaltypechatmodelnomic-ai/nomic-embed-text-v1.5gen_paramsstartfinishipconv_id03037905b8a749e399e111e32c19baeemodel_namenomic-ai/nomic-embed-text-v1.5promptwhat is the difference between an electronic signal and an electromagnetic signal?outputwhat is the difference between an electronic signal and an electromagnetic signal?Askubuntu Stackexchange\n\nQ: Trouble Installing libxp6 and multi-arch-support I am following this guide, but I have run into some difficulties at step 11. When I type\nsudo apt-get install ./libxp6_1.0.2-2_amd64.deb\n\nI get\nReading package lists... Done\nBuilding dependency tree \nReading state information... Done\nNote, selecting 'libxp6' instead of './libxp6_1.0.2-2_amd64.deb'\nSome packages could not be installed. This may mean that you have\nrequested an impossible situation or if you are using the unstable\ndistribution that some required packages have not yet been created\nor been moved out of Incoming.\nThe following information may help to resolve the situation:\n\nThe following packages have unmet dependencies:\n libxp6 : PreDepends: multiarch-support but it is not installable\nE: Unable to correct problems, you have held broken packages.\n\nlibxp6 and multiarch-support are what seem to be giving me difficulties. Searching on the web has given me a little, but not enough to install. Even the Ubuntu package website isn't helping.\nAny help would be appreciated.\n\nA: Then you have to install multiarch-support package from 18.04 LTS first with\ncd ~/Downloads\nwget http://archive.ubuntu.com/ubuntu/pool/main/g/glibc/multiarch-support_2.27-3ubuntu1_amd64.deb\nsudo apt-get install ./multiarch-support_2.27-3ubuntu1_amd64.deb\n\nand continue to follow the guide.\n"]], "corpus": "stackexchange"} |
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{"tstamp": 1722305223.7026, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722305223.4442, "finish": 1722305223.7026, "ip": "", "conv_id": "9f4dfa402ea64c74bfec3768569b6d00", "model_name": "GritLM/GritLM-7B", "prompt": "How can deep learning be used to design communication systems as end-to-end reconstruction tasks? ", "output": [["How can deep learning be used to design communication systems as end-to-end reconstruction tasks? ", "Title: An Introduction to Deep Learning for the Physical Layer\n\nAbstract: We present and discuss several novel applications of deep learning for the physical layer. By interpreting a communications system as an autoencoder, we develop a fundamental new way to think about communications system design as an end-to-end reconstruction task that seeks to jointly optimize transmitter and receiver components in a single process. We show how this idea can be extended to networks of multiple transmitters and receivers and present the concept of radio transformer networks as a means to incorporate expert domain knowledge in the machine learning model. Lastly, we demonstrate the application of convolutional neural networks on raw IQ samples for modulation classification which achieves competitive accuracy with respect to traditional schemes relying on expert features. The paper is concluded with a discussion of open challenges and areas for future investigation."]], "corpus": "arxiv"} |
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{"tstamp": 1722305223.7026, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722305223.4442, "finish": 1722305223.7026, "ip": "", "conv_id": "fb3390a4534f41758663e225ee7ed9a6", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "How can deep learning be used to design communication systems as end-to-end reconstruction tasks? ", "output": [["How can deep learning be used to design communication systems as end-to-end reconstruction tasks? ", "Title: Design of Communication Systems using Deep Learning: A Variational Inference Perspective\n\nAbstract: Recent research in the design of end to end communication system using deep learning has produced models which can outperform traditional communication schemes. Most of these architectures leveraged autoencoders to design the encoder at the transmitter and decoder at the receiver and train them jointly by modeling transmit symbols as latent codes from the encoder. However, in communication systems, the receiver has to work with noise corrupted versions of transmit symbols. Traditional autoencoders are not designed to work with latent codes corrupted with noise. In this work, we provide a framework to design end to end communication systems which accounts for the existence of noise corrupted transmit symbols. The proposed method uses deep neural architecture. An objective function for optimizing these models is derived based on the concepts of variational inference. Further, domain knowledge such as channel type can be systematically integrated into the objective. Through numerical simulation, the proposed method is shown to consistently produce models with better packing density and achieving it faster in multiple popular channel models as compared to the previous works leveraging deep learning models."]], "corpus": "arxiv"} |
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{"tstamp": 1722305229.7963, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722305229.6152, "finish": 1722305229.7963, "ip": "", "conv_id": "6cf9d4db20f8436c8c7f2ccaca29d2fa", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "what is the difference between cluster point and limit point?", "output": [["what is the difference between cluster point and limit point?", "Askubuntu Stackexchange\n\nQ: How to enable user sharing per instructions? I have this entry in .xsession-errors log\n\"Nautilus-Share-Message: Called \"net usershare info\" but it failed: 'net usershare' returned \nerror 255: net usershare: cannot open usershare directory /var/lib/samba/usershares. \nError No such file or directory\nPlease ask your system administrator to enable user sharing.\n\nThis is in relation to my applets having to be reloaded after every boot. Just wondering how I would enable user sharing, and how it affects my applets ??\n\nA: I don't think this effects your applets at all, that's probably a different bug. You could just make the directory with sudo:\nsudo mkdir -p /var/lib/samba/usershares\n\nBut I don't really know what it will do.\ncorpusstackexchange |
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tstamptask_typeretrievaltypechatmodelBAAI/bge-large-en-v1.5gen_paramsstartfinishipconv_id38308d8cc72f4a24b8ddc6bafec79a96model_nameBAAI/bge-large-en-v1.5promptwhat is the difference between cluster point and limit point?outputwhat is the difference between cluster point and limit point?Math Stackexchange\n\nQ: Limit and limit points What is the basic difference between limit and limit points, and if a sequence has one unique limit how it can have a lot of limit points \n\nA: A limit point is a generalization of a limit (each limit is a limit point but not vice versa). You can see this in the definition:\nLimit: $a$ is a limit of $(a_n)$, iff in each neighborhood of $a$ are almost all elements of $(a_n)$.\nLimit point: $a$ is a limit of $(a_n)$, iff in each neighborhood of $a$ are infinite elements of $(a_n)$.\nI highlighted the difference between both definitions.\nBecause only infinite elements need to be in any neighborhood of $a$. There can be infinite elements outside this neighborhood which can clustered to another limit point. This is the reason why there might be many limit points for a sequence. If $a$ is a limit only finite elements can be outside any given neighborhood which is not enough to go to another limit.\ncorpusstackexchange |
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tstamptask_typeretrievaltypechatmodelBM25gen_paramsstartfinishipconv_idd56443b4b3064e69a3fc99eabc8fbf8fmodel_nameBM25promptmass effect 1 how many skill points total?outputmass effect 1 how many skill points total?Gaming Stackexchange\n\nQ: How many skill points will I have at max level? What is the maximum level in Mass Effect 3?\nHow many skill points will Shepard have at max level? \nIf NPC companions earn a different number of skill points than Shepard, what's the number they'll have at max level?\n\nA: Since this question seems to have been deemed the Canonical by mods:\nThe maximum character level is 60. At this level, Shepard will have earned a total of 181 Skill Points.\nMaxing a single skill requires 21 points.\nThere are enough points in the game to max out a total of 8 skills, with a few left over.\nShepard, conveniently enough, has 8 skills, plus one slot for a Bonus Power.\nSo, you can come very close to maxing out all skills, but you can't quite make it to maxing out all 9. You can max out 8 of them, and have 13 points remaining for the 9th, which lets you get to rank 4. You'll have 2 points left over. Alternately, you could max out 7 skills, get two to rank 5, and have 3 points left over.\ncorpusstackexchange |
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tstamptask_typeretrievaltypechatmodelmixedbread-ai/mxbai-embed-large-v1gen_paramsstartfinishipconv_id2766c8817d564f3fa47a1aa8eeaa0b03model_namemixedbread-ai/mxbai-embed-large-v1promptmass effect 1 how many skill points total?outputmass effect 1 how many skill points total?Gaming Stackexchange\n\nQ: How many skill points will I have at max level? What is the maximum level in Mass Effect 3?\nHow many skill points will Shepard have at max level? \nIf NPC companions earn a different number of skill points than Shepard, what's the number they'll have at max level?\n\nA: Since this question seems to have been deemed the Canonical by mods:\nThe maximum character level is 60. At this level, Shepard will have earned a total of 181 Skill Points.\nMaxing a single skill requires 21 points.\nThere are enough points in the game to max out a total of 8 skills, with a few left over.\nShepard, conveniently enough, has 8 skills, plus one slot for a Bonus Power.\nSo, you can come very close to maxing out all skills, but you can't quite make it to maxing out all 9. You can max out 8 of them, and have 13 points remaining for the 9th, which lets you get to rank 4. You'll have 2 points left over. Alternately, you could max out 7 skills, get two to rank 5, and have 3 points left over.\ncorpusstackexchange |
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tstamptask_typeretrievaltypechatmodelGritLM/GritLM-7Bgen_paramsstartfinishipconv_idbc1a7d6500334478abf4839584bbeaecmodel_nameGritLM/GritLM-7BpromptHow does the Brain Network Transformer leverage connection profiles and orthonormal clustering to analyze mental disorders?outputHow does the Brain Network Transformer leverage connection profiles and orthonormal clustering to analyze mental disorders?Title: Brain Network Transformer\n\nAbstract: Human brains are commonly modeled as networks of Regions of Interest (ROIs) and their connections for the understanding of brain functions and mental disorders. Recently, Transformer-based models have been studied over different types of data, including graphs, shown to bring performance gains widely. In this work, we study Transformer-based models for brain network analysis. Driven by the unique properties of data, we model brain networks as graphs with nodes of fixed size and order, which allows us to (1) use connection profiles as node features to provide natural and low-cost positional information and (2) learn pair-wise connection strengths among ROIs with efficient attention weights across individuals that are predictive towards downstream analysis tasks. Moreover, we propose an Orthonormal Clustering Readout operation based on self-supervised soft clustering and orthonormal projection. This design accounts for the underlying functional modules that determine similar behaviors among groups of ROIs, leading to distinguishable cluster-aware node embeddings and informative graph embeddings. Finally, we re-standardize the evaluation pipeline on the only one publicly available large-scale brain network dataset of ABIDE, to enable meaningful comparison of different models. Experiment results show clear improvements of our proposed Brain Network Transformer on both the public ABIDE and our restricted ABCD datasets. The implementation is available at https://github.com/Wayfear/BrainNetworkTransformer.corpusarxiv |
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tstamptask_typeretrievaltypechatmodelsentence-transformers/all-MiniLM-L6-v2gen_paramsstartfinishipconv_id0848dc0be9b94d688857e785d82e9f51model_namesentence-transformers/all-MiniLM-L6-v2promptHow does the Brain Network Transformer leverage connection profiles and orthonormal clustering to analyze mental disorders?outputHow does the Brain Network Transformer leverage connection profiles and orthonormal clustering to analyze mental disorders?Title: Noncompact, Gauge-Invariant Simulations of U(1), SU(2), and SU(3)\n\nAbstract: We have applied a new noncompact, gauge-invariant, Monte Carlo method to simulate the U(1), SU(2), and SU(3) gauge theories on 8^4 and 12^4 lattices. For U(1) the Creutz ratios of the Wilson loops agree with the exact results for beta > 0.5 after a renormalization of the charge. The SU(2) and SU(3) Creutz ratios robustly display quark confinement at beta = 0.5 and beta = 2, respectively. At much weaker coupling, the SU(2) and SU(3) Creutz ratios agree with perturbation theory after a renormalization of the coupling constant. For SU(3) without quarks, our lattice QCD parameter is Lambda_L = 130 +- 18$ MeV.corpusarxiv |
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tstamptask_typeretrievaltypechatmodelmixedbread-ai/mxbai-embed-large-v1gen_paramsstartfinishipconv_idebb67aaa6367404fada798faa16238bemodel_namemixedbread-ai/mxbai-embed-large-v1promptcan gravity waves travel faster than light?outputcan gravity waves travel faster than light?Physics Stackexchange\n\nQ: Why can't the speed of gravitational waves be greater than the speed of light if the universe can expand faster than the speed of light? Since the expansion speed of the universe can be greater than the speed of light, why can't gravitational waves, which also uses space as the medium, travel faster than the speed of light?\n\nA: Gravitational waves are solutions to the linearized field equations\n$\\Box h_{\\mu \\nu} = 0,$\nwhere $\\Box$ is the d'Alembertian differential operator.\nThey are of the form\n\\begin{align}\nh_{\\mu \\nu}= \\text{Re}[H_{\\mu \\nu} e^{i k_{\\rho} x^{\\rho}}] , \n\\end{align}\nwhere $H_{\\mu \\nu}$ is just a complex symmetric matrix setting the polarization of the wave and $k_{\\rho}$ is its wave-vector.\nFor the above ansatz to satisfy the equations, we plug it into the equations to get the condition $k_{\\mu} k^{\\mu} =0$. This is just the statement that the wave-vector must be null, meaning the wave propagates at the speed of light.\n"]], "corpus": "stackexchange"} |
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{"tstamp": 1722305896.3763, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722305894.1942, "finish": 1722305896.3763, "ip": "", "conv_id": "0594d3384acf4283a4656c03b325081a", "model_name": "BM25", "prompt": "can gravity waves travel faster than light?", "output": [["can gravity waves travel faster than light?", "Physics Stackexchange\n\nQ: Why can't fermions be affected by effective gravity in non-linear quantum electrodynamics? Quantum electrodynamics based upon Euler-Heisenberg or Born-Infeld Lagrangians predict photons\nto move according to an effective metric which is dependent on the background electromagnetic\nfield. In other words, photon trajectories are curved in presence of electromagnetic fields,\nmeaning that an effective gravity is acting upon. If part of fermion masses is allegedly of\nelectromagnetic origin, the question why their trajectories are not affected by this\neffective gravity naturally comes to mind.\n\nA: In the presence of a background electromagnetic field, electromagnetic fields travel along a deformed light cone which is smaller than the \. However, charged fermions can still travel faster than electromagnetic waves as long as they are still slower than the \. They emit Cherenkov radiation while doing so. \ncorpusstackexchange |
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tstamptask_typeretrievaltypechatmodelAlibaba-NLP/gte-Qwen2-7B-instructgen_paramsstartfinishipconv_id5993407c26d04c54981a49217a38518dmodel_nameAlibaba-NLP/gte-Qwen2-7B-instructpromptwhen did scotland last qualify for world cupoutputwhen did scotland last qualify for world cupScotland national football team\n\nScotland lost just two of their first 43 international matches. It was not until a 2–0 home defeat by Ireland in 1903 that Scotland lost a match to a team other than England. This run of success meant that Scotland would have regularly topped the Elo ratings, which were calculated in 1998, between 1876 and 1904. Scotland won the British Home Championship outright on 24 occasions, and shared the title 17 times with at least one other team. A noteworthy victory for Scotland before the Second World War was the 5–1 victory over England in 1928, which led to that Scotland side being known as the \. Scotland played their first match outside the British Isles in 1929, beating Norway 7–3 in Bergen. Scotland continued to contest regular friendly matches against European opposition and enjoyed wins against Germany and France before losing to the Austrian \ and Italy in 1931.\nScotland, like the other Home Nations, did not enter the three FIFA World Cups held during the 1930s. This was because the four associations had been excluded from FIFA due to a disagreement regarding the status of amateur players. The four associations, including Scotland, returned to the FIFA fold after the Second World War. A match between a United Kingdom team and a \ team was played at Hampden Park in 1947 to celebrate this reconciliation.\n1950s: Entering the World Cupcorpuswikipedia |
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tstamptask_typeretrievaltypechatmodelSalesforce/SFR-Embedding-2_Rgen_paramsstartfinishipconv_id940948f56e9c4f5297cb17f69935bd8bmodel_nameSalesforce/SFR-Embedding-2_Rpromptwhen did scotland last qualify for world cupoutputwhen did scotland last qualify for world cupScotland at the FIFA World Cup\n\nThe World Cup consists of two parts, the qualification phase and the final phase (officially called the World Cup Finals). The qualification phase, which currently take place over the three years preceding the Finals, is used to determine which teams qualify for the Finals. The current format of the Finals involves 32 teams competing at venues within the host nation (or nations) over a period of about a month. The World Cup Finals is the most widely viewed sporting event in the world, with an estimated 715.1 million people watching the 2006 tournament final.\nHistory\n1930s\nScotland did not compete in the first three World Cup competitions, in 1930, 1934 and 1938. Because of a dispute with FIFA over \ payments to players, the SFA, with the football associations of England, Ireland and Wales, withdrew from FIFA in 1928, and did not rejoin as a permanent member until 1946.\n1950s\nThe readmission of the Scottish Football Association to FIFA in 1946 meant that Scotland were now eligible to enter the 1950 FIFA World Cup. FIFA advised that places would be awarded to the top two teams in the 1949–50 British Home Championship, but the SFA announced that Scotland would attend the finals only if Scotland won the competition. Scotland won their first two matches, but a 1–0 home defeat by England meant that the Scots finished as runners-up. The SFA stood by this proclamation, despite pleas to the contrary by the Scotland players, supported by England captain Billy Wright and the other England players. Sportswriter Brian Glanville later described the SFA as showing \ in their refusal to send a team to the World Cup.corpuswikipedia |
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tstamptask_typeretrievaltypechatmodeltext-embedding-3-largegen_paramsstartfinishipconv_idbe3fc968a3aa487ba8db65457d91fec3model_nametext-embedding-3-largepromptMethods for tracing dataset provenance and legal implications in AI training data outputMethods for tracing dataset provenance and legal implications in AI training data Title: The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing & Attribution in AI\n\nAbstract: The race to train language models on vast, diverse, and inconsistently documented datasets has raised pressing concerns about the legal and ethical risks for practitioners. To remedy these practices threatening data transparency and understanding, we convene a multi-disciplinary effort between legal and machine learning experts to systematically audit and trace 1800+ text datasets. We develop tools and standards to trace the lineage of these datasets, from their source, creators, series of license conditions, properties, and subsequent use. Our landscape analysis highlights the sharp divides in composition and focus of commercially open vs closed datasets, with closed datasets monopolizing important categories: lower resource languages, more creative tasks, richer topic variety, newer and more synthetic training data. This points to a deepening divide in the types of data that are made available under different license conditions, and heightened implications for jurisdictional legal interpretations of copyright and fair use. We also observe frequent miscategorization of licenses on widely used dataset hosting sites, with license omission of 70%+ and error rates of 50%+. This points to a crisis in misattribution and informed use of the most popular datasets driving many recent breakthroughs. As a contribution to ongoing improvements in dataset transparency and responsible use, we release our entire audit, with an interactive UI, the Data Provenance Explorer, which allows practitioners to trace and filter on data provenance for the most popular open source finetuning data collections: www.dataprovenance.org.corpusarxiv |
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tstamptask_typeretrievaltypechatmodelBAAI/bge-large-en-v1.5gen_paramsstartfinishipconv_idff92d927e7a1440f91ca6ddae1fb40a5model_nameBAAI/bge-large-en-v1.5promptMethods for tracing dataset provenance and legal implications in AI training data outputMethods for tracing dataset provenance and legal implications in AI training data Title: The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing & Attribution in AI\n\nAbstract: The race to train language models on vast, diverse, and inconsistently documented datasets has raised pressing concerns about the legal and ethical risks for practitioners. To remedy these practices threatening data transparency and understanding, we convene a multi-disciplinary effort between legal and machine learning experts to systematically audit and trace 1800+ text datasets. We develop tools and standards to trace the lineage of these datasets, from their source, creators, series of license conditions, properties, and subsequent use. Our landscape analysis highlights the sharp divides in composition and focus of commercially open vs closed datasets, with closed datasets monopolizing important categories: lower resource languages, more creative tasks, richer topic variety, newer and more synthetic training data. This points to a deepening divide in the types of data that are made available under different license conditions, and heightened implications for jurisdictional legal interpretations of copyright and fair use. We also observe frequent miscategorization of licenses on widely used dataset hosting sites, with license omission of 70%+ and error rates of 50%+. This points to a crisis in misattribution and informed use of the most popular datasets driving many recent breakthroughs. As a contribution to ongoing improvements in dataset transparency and responsible use, we release our entire audit, with an interactive UI, the Data Provenance Explorer, which allows practitioners to trace and filter on data provenance for the most popular open source finetuning data collections: www.dataprovenance.org.corpusarxiv |
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tstamptask_typeretrievaltypechatmodelvoyage-multilingual-2gen_paramsstartfinishipconv_idf027bc84f35a4d748c67b44a2435bbe4model_namevoyage-multilingual-2promptWhat is a transformer?outputWhat is a transformer?Title: Formal Algorithms for Transformers\n\nAbstract: This document aims to be a self-contained, mathematically precise overview of transformer architectures and algorithms (*not* results). It covers what transformers are, how they are trained, what they are used for, their key architectural components, and a preview of the most prominent models. The reader is assumed to be familiar with basic ML terminology and simpler neural network architectures such as MLPs.corpusarxiv |
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tstamptask_typeretrievaltypechatmodelmixedbread-ai/mxbai-embed-large-v1gen_paramsstartfinishipconv_ida55ff04a15fd4107ac10365fbb0eb9a2model_namemixedbread-ai/mxbai-embed-large-v1promptwhere did the british empire control an entire continentoutputwhere did the british empire control an entire continentBritish Empire\n\nThe British Empire comprised the dominions, colonies, protectorates, mandates, and other territories ruled or administered by the United Kingdom and its predecessor states. It began with the overseas possessions and trading posts established by England in the late 16th and early 17th centuries. At its height in the 19th and early 20th centuries, it was the largest empire in history and, for a century, was the foremost global power. By 1913, the British Empire held sway over 412 million people, of the world population at the time, and by 1920, it covered , of the Earth's total land area. As a result, its constitutional, legal, linguistic, and cultural legacy is widespread. At the peak of its power, it was described as \"the empire on which the sun never sets\", as the sun was always shining on at least one of its territories.\nDuring the Age of Discovery in the 15th and 16th centuries, Portugal and Spain pioneered European exploration of the globe, and in the process established large overseas empires. Envious of the great wealth these empires generated, England, France, and the Netherlands began to establish colonies and trade networks of their own in the Americas and Asia. A series of wars in the 17th and 18th centuries with the Netherlands and France left England (Britain, following the 1707 Act of Union with Scotland) the dominant colonial power in North America. Britain became a major power in the Indian subcontinent after the East India Company's conquest of Mughal Bengal at the Battle of Plassey in 1757.corpuswikipedia |
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tstamptask_typeretrievaltypechatmodelsentence-transformers/all-MiniLM-L6-v2gen_paramsstartfinishipconv_ida6afd35e6fa54368be503cb1c3cf43eamodel_namesentence-transformers/all-MiniLM-L6-v2promptwhere did the british empire control an entire continentoutputwhere did the british empire control an entire continentTerritorial evolution of the British Empire\n\nThe territorial evolution of the British Empire is considered to have begun with the foundation of the English colonial empire in the late 16th century. Since then, many territories around the world have been under the control of the United Kingdom or its predecessor states.\nWhen the Kingdom of Great Britain was formed in 1707 by the union of the Kingdoms of Scotland and England, the latter country's colonial possessions passed to the new state. Similarly, when Great Britain was united with the Kingdom of Ireland in 1801 to form the United Kingdom, control over its colonial possessions passed to the latter state. Collectively, these territories are referred to as the British Empire. When much of Ireland gained independence in 1922 as the Irish Free State, the other territories of the empire remained under the control of the United Kingdom.\nFrom 1714 to 1837, the British throne was held by a series of kings who were also the rulers of the German state of Hanover. However, this was purely a personal union, with Hanover maintaining its political independence otherwise, and so it is not usually considered to have formed part of the British Empire."]], "corpus": "wikipedia"} |
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{"tstamp": 1722306986.0121, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722306985.8309, "finish": 1722306986.0121, "ip": "", "conv_id": "734d33bae026481cab60cb1e123a36b4", "model_name": "GritLM/GritLM-7B", "prompt": "Is attention all I need?", "output": [["Is attention all I need?", "Title: Attention Is All You Need\n\nAbstract: The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data."]], "corpus": "arxiv"} |
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{"tstamp": 1722307003.9939, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722307003.6084, "finish": 1722307003.9939, "ip": "", "conv_id": "3c77700a297d4a26b9466b34301c929b", "model_name": "voyage-multilingual-2", "prompt": "Is attention all I need?", "output": [["Is attention all I need?", "Title: Visual Attention and its Intimate Links to Spatial Cognition\n\nAbstract: It is almost universal to regard attention as the facility that permits an agent, human or machine, to give priority processing resources to relevant stimuli while ignoring the irrelevant. The reality of how this might manifest itself throughout all the forms of perceptual and cognitive processes possessed by humans, however, is not as clear. Here we examine this reality with a broad perspective in order to highlight the myriad ways that attentional processes impact both perception and cognition. The paper concludes by showing two real world problems that exhibit sufficient complexity to illustrate the ways in which attention and cognition connect. These then point to new avenues of research that might illuminate the overall cognitive architecture of spatial cognition."]], "corpus": "arxiv"} |
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{"tstamp": 1722307006.8942, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722307006.267, "finish": 1722307006.8942, "ip": "", "conv_id": "fdbaddd493b84cc89357c4480e2d727d", "model_name": "voyage-multilingual-2", "prompt": "The effect of Long pimple ", "output": [["The effect of Long pimple ", "Pimple\n\nA pimple or zit is a kind of comedo that results from excess sebum and dead skin cells getting trapped in the pores of the skin. In its aggravated state, it may evolve into a pustule or papule. Pimples can be treated by acne medications, antibiotics, and anti-inflammatories prescribed by a physician, or various over the counter remedies purchased at a pharmacy.\nCauses\nSebaceous glands inside the pore of the skin produce sebum. When the outer layers of skin shed (a natural and continuous process, normally), dead skin and oily sebum left behind may bond together and form a blockage of the sebaceous gland at the base of the skin. This is most common when the skin becomes thicker at puberty. The sebaceous gland continues to produce sebum, which builds up behind the blockage, allowing bacteria to grow in the area, including the species Staphylococcus aureus and Cutibacterium acnes, which causes inflammation and infection. Other causes of pimples include family history, stress, fluctuations in hormone levels, hair and skincare products, medication side effects, and un-diagnosed or underlying medical conditions. Pimples can be part of the presentation of rosacea."]], "corpus": "wikipedia"} |
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{"tstamp": 1722307006.8942, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722307006.267, "finish": 1722307006.8942, "ip": "", "conv_id": "86047e9633f3430da6d888564b950f65", "model_name": "text-embedding-3-large", "prompt": "The effect of Long pimple ", "output": [["The effect of Long pimple ", "Table tennis grips and playing styles\n\nShort pips hitter This penhold style utilizes a short pips out rubber, usually on only one side of the racket with no backside rubber. Short pips hitters play over the table, hitting the ball very flatly as soon as it bounces off the table with the pips being largely unaffected by (or even negating) the majority of the opponent's spin. The advantage of hitting the ball flatly is that all of the player's power is transferred into the linear velocity of the ball, without expending any energy on creating spin. This means that the shots are very fast, and typically hard to block for the inexperienced player. However, the lack of a topspin Magnus effect means that the shots are harder to place on the table since they don't follow a downward arc trajectory, and the lack of angular momentum on the ball means that shots are less consistent and more sensitive to small amounts of wind or air pressure, often giving a knuckle ball effect.\nNotable penhold short pips hitters include 1996 Olympic Champion Liu Guoliang, 1985 and 1987 World Champion Jiang Jialiang, three-time Olympian Toshio Tasaki, Yang Ying, Kwak Bang-bang, Seok Eun-mi, He Zhi Wen, Lee Eun-hee, Wang Zeng Yi and Rory Cargill.\nShakehand playing stylescorpuswikipedia |
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tstamptask_typeretrievaltypechatmodeltext-embedding-3-largegen_paramsstartfinishipconv_ida10afac3a30a4e82b4163e080b19e9e9model_nametext-embedding-3-largepromptIs attention all I need?outputIs attention all I need?Title: Is Attention All What You Need? -- An Empirical Investigation on Convolution-Based Active Memory and Self-Attention\n\nAbstract: The key to a Transformer model is the self-attention mechanism, which allows the model to analyze an entire sequence in a computationally efficient manner. Recent work has suggested the possibility that general attention mechanisms used by RNNs could be replaced by active-memory mechanisms. In this work, we evaluate whether various active-memory mechanisms could replace self-attention in a Transformer. Our experiments suggest that active-memory alone achieves comparable results to the self-attention mechanism for language modelling, but optimal results are mostly achieved by using both active-memory and self-attention mechanisms together. We also note that, for some specific algorithmic tasks, active-memory mechanisms alone outperform both self-attention and a combination of the two.corpusarxiv |
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tstamptask_typeretrievaltypechatmodeltext-embedding-3-largegen_paramsstartfinishipconv_id3fe38b7d37f6440f9394145fd67cb9a9model_nametext-embedding-3-largepromptThe effect of long Pimple outputThe effect of long Pimple Table tennis grips and playing styles\n\nShort pips hitter This penhold style utilizes a short pips out rubber, usually on only one side of the racket with no backside rubber. Short pips hitters play over the table, hitting the ball very flatly as soon as it bounces off the table with the pips being largely unaffected by (or even negating) the majority of the opponent's spin. The advantage of hitting the ball flatly is that all of the player's power is transferred into the linear velocity of the ball, without expending any energy on creating spin. This means that the shots are very fast, and typically hard to block for the inexperienced player. However, the lack of a topspin Magnus effect means that the shots are harder to place on the table since they don't follow a downward arc trajectory, and the lack of angular momentum on the ball means that shots are less consistent and more sensitive to small amounts of wind or air pressure, often giving a knuckle ball effect.\nNotable penhold short pips hitters include 1996 Olympic Champion Liu Guoliang, 1985 and 1987 World Champion Jiang Jialiang, three-time Olympian Toshio Tasaki, Yang Ying, Kwak Bang-bang, Seok Eun-mi, He Zhi Wen, Lee Eun-hee, Wang Zeng Yi and Rory Cargill.\nShakehand playing styles"]], "corpus": "wikipedia"} |
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{"tstamp": 1722307112.9208, "task_type": "retrieval", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722307112.5638, "finish": 1722307112.9208, "ip": "", "conv_id": "3754c3b0f9fd448eac01d2cebd94e498", "model_name": "embed-english-v3.0", "prompt": "Is attention all I need?", "output": [["Is attention all I need?", "Title: Attention is all they need: Cognitive science and the (techno)political economy of attention in humans and machines\n\nAbstract: This paper critically analyses the \"attention economy\" within the framework of cognitive science and techno-political economics, as applied to both human and machine interactions. We explore how current business models, particularly in digital platform capitalism, harness user engagement by strategically shaping attentional patterns. These platforms utilize advanced AI and massive data analytics to enhance user engagement, creating a cycle of attention capture and data extraction. We review contemporary (neuro)cognitive theories of attention and platform engagement design techniques and criticize classical cognitivist and behaviourist theories for their inadequacies in addressing the potential harms of such engagement on user autonomy and wellbeing. 4E approaches to cognitive science, instead, emphasizing the embodied, extended, enactive, and ecological aspects of cognition, offer us an intrinsic normative standpoint and a more integrated understanding of how attentional patterns are actively constituted by adaptive digital environments. By examining the precarious nature of habit formation in digital contexts, we reveal the techno-economic underpinnings that threaten personal autonomy by disaggregating habits away from the individual, into an AI managed collection of behavioural patterns. Our current predicament suggests the necessity of a paradigm shift towards an ecology of attention. This shift aims to foster environments that respect and preserve human cognitive and social capacities, countering the exploitative tendencies of cognitive capitalism."]], "corpus": "arxiv"} |
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{"tstamp": 1722307122.7157, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722307120.4275, "finish": 1722307122.7157, "ip": "", "conv_id": "ea468cdfbe3a4dd680a9a7471bed7154", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "The effect of long pimple", "output": [["The effect of long pimple", "Table tennis grips and playing styles\n\nShort pips hitter This penhold style utilizes a short pips out rubber, usually on only one side of the racket with no backside rubber. Short pips hitters play over the table, hitting the ball very flatly as soon as it bounces off the table with the pips being largely unaffected by (or even negating) the majority of the opponent's spin. The advantage of hitting the ball flatly is that all of the player's power is transferred into the linear velocity of the ball, without expending any energy on creating spin. This means that the shots are very fast, and typically hard to block for the inexperienced player. However, the lack of a topspin Magnus effect means that the shots are harder to place on the table since they don't follow a downward arc trajectory, and the lack of angular momentum on the ball means that shots are less consistent and more sensitive to small amounts of wind or air pressure, often giving a knuckle ball effect.\nNotable penhold short pips hitters include 1996 Olympic Champion Liu Guoliang, 1985 and 1987 World Champion Jiang Jialiang, three-time Olympian Toshio Tasaki, Yang Ying, Kwak Bang-bang, Seok Eun-mi, He Zhi Wen, Lee Eun-hee, Wang Zeng Yi and Rory Cargill.\nShakehand playing stylescorpuswikipedia |
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tstamptask_typeretrievaltypechatmodelGritLM/GritLM-7Bgen_paramsstartfinishipconv_idef28c889daf44e7587446e62092e3d2amodel_nameGritLM/GritLM-7BpromptIs attention all I need?outputIs attention all I need?Title: Attention Is All You Need\n\nAbstract: The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.corpusarxiv |
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