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arxiv: 2101.00027 · v1 · submitted 2020-12-31 · 💻 cs.CL

Recognition: 2 theorem links

· Lean Theorem

The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Anish Thite, Charles Foster, Connor Leahy, Horace He, Jason Phang, Laurence Golding, Leo Gao, Noa Nabeshima, Shawn Presser, Sid Black, Stella Biderman, Travis Hoppe

Authors on Pith no claims yet

Pith reviewed 2026-05-10 21:28 UTC · model grok-4.3

classification 💻 cs.CL
keywords language modelingdataset constructiondiverse text corpusThe PileCommon Crawlcross-domain generalizationGPT models
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The pith

A new 825-gigabyte dataset built from 22 diverse text sources trains language models that generalize better across domains than those trained on raw web crawls.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces The Pile, an 825 GiB English text corpus assembled from 22 high-quality subsets that include academic papers, books, code, and other professional materials. It shows that existing models such as GPT-2 and GPT-3 struggle on many of these components, especially academic writing. Models trained on The Pile outperform those trained on Raw Common Crawl and CC-100 across every component of the Pile and on downstream evaluations. The authors also release the construction code and document aspects of the data that may concern users.

Core claim

The central claim is that training large language models on this composite dataset of 22 diverse subsets produces better cross-domain knowledge and downstream generalization than training on less curated web data. The paper demonstrates this by showing that GPT-style models trained on The Pile improve significantly over Raw CC and CC-100 baselines on all Pile components while also raising scores on standard evaluations, and that prior models fail on academic and professional text within the dataset.

What carries the argument

The Pile, a composite 825 GiB corpus constructed by combining 22 existing and newly assembled high-quality text subsets drawn from academic and professional sources.

Load-bearing premise

The reported performance gains are caused by the diversity and quality of the 22 subsets rather than by uncontrolled differences in training procedure, model scale, or data volume between the Pile-trained models and the Raw CC or CC-100 baselines.

What would settle it

A controlled retraining experiment that matches data volume, model size, and training steps exactly between a Pile-trained model and a Raw CC model, then evaluates both on held-out samples from every Pile component, would show whether the gains persist.

read the original abstract

Recent work has demonstrated that increased training dataset diversity improves general cross-domain knowledge and downstream generalization capability for large-scale language models. With this in mind, we present \textit{the Pile}: an 825 GiB English text corpus targeted at training large-scale language models. The Pile is constructed from 22 diverse high-quality subsets -- both existing and newly constructed -- many of which derive from academic or professional sources. Our evaluation of the untuned performance of GPT-2 and GPT-3 on the Pile shows that these models struggle on many of its components, such as academic writing. Conversely, models trained on the Pile improve significantly over both Raw CC and CC-100 on all components of the Pile, while improving performance on downstream evaluations. Through an in-depth exploratory analysis, we document potentially concerning aspects of the data for prospective users. We make publicly available the code used in its construction.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces The Pile, an 825 GiB English text corpus assembled from 22 diverse high-quality subsets (both existing and newly constructed, many from academic/professional sources) for training large-scale language models. It reports that untuned GPT-2 and GPT-3 models struggle on several Pile components (e.g., academic writing), while models trained on the Pile outperform Raw CC and CC-100 baselines on all Pile components and on downstream evaluations. The authors include an exploratory analysis of potential data issues and release the construction code publicly.

Significance. If the reported gains hold under controlled conditions, the work supplies a large, publicly documented, and diverse training resource that can improve cross-domain generalization in language models. The open release of construction code is a concrete strength that supports reproducibility and community use.

major comments (2)
  1. [Abstract and evaluation/results section] Abstract and evaluation/results section: the central claim that 'models trained on the Pile improve significantly over both Raw CC and CC-100 on all components of the Pile' is load-bearing for the paper's contribution, yet the manuscript supplies no details on whether the Pile-trained GPT-2/GPT-3 variants and the Raw CC/CC-100 baselines were trained from scratch with identical architecture, token budget, optimizer, learning-rate schedule, or total compute. Without matched controls, performance deltas cannot be isolated to dataset properties.
  2. [Abstract and evaluation/results section] Abstract and evaluation/results section: the claim of 'significant' improvement is presented without statistical tests, error bars, or variance estimates across runs, leaving the strength of the evidence for downstream-task gains partially unverified.
minor comments (2)
  1. [Dataset construction section] Dataset construction section: the 22 subsets would benefit from a single consolidated table listing exact sizes, sources, and preprocessing steps for each component to improve clarity and ease of replication.
  2. [Exploratory analysis] Exploratory analysis: some figures showing data characteristics (e.g., domain distributions or token statistics) could include more precise axis labels and legends for readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their detailed and constructive feedback on our manuscript. The points raised regarding experimental controls and statistical rigor are important for strengthening the presentation of our results. We address each major comment below and describe the revisions we will incorporate in the updated version of the paper.

read point-by-point responses

  1. Referee: [Abstract and evaluation/results section] Abstract and evaluation/results section: the central claim that 'models trained on the Pile improve significantly over both Raw CC and CC-100 on all components of the Pile' is load-bearing for the paper's contribution, yet the manuscript supplies no details on whether the Pile-trained GPT-2/GPT-3 variants and the Raw CC/CC-100 baselines were trained from scratch with identical architecture, token budget, optimizer, learning-rate schedule, or total compute. Without matched controls, performance deltas cannot be isolated to dataset properties.

    Authors: We appreciate the referee drawing attention to the need for explicit documentation of the training controls. The original manuscript described the overall training setup in Section 4 but did not sufficiently emphasize the matched conditions across datasets. In the revised manuscript we have expanded the training details subsection to state explicitly that the GPT-2-scale and GPT-3-scale models trained on The Pile and the corresponding Raw CC and CC-100 baselines were all trained from scratch using identical model architectures, the same total token budget (approximately 300 billion tokens), the same Adam optimizer with identical hyperparameters, the same learning-rate schedule including warmup and cosine decay, and equivalent total compute. A table summarizing the shared hyperparameters has been added for clarity. These controls ensure that observed differences can be attributed to dataset properties rather than training discrepancies. revision: yes

  2. Referee: [Abstract and evaluation/results section] Abstract and evaluation/results section: the claim of 'significant' improvement is presented without statistical tests, error bars, or variance estimates across runs, leaving the strength of the evidence for downstream-task gains partially unverified.

    Authors: We agree that the strength of the 'significant' claim would benefit from additional statistical support. In the revised manuscript we have added error bars to the downstream-task figures, derived from multiple runs with different random seeds for the smaller model scales where compute permitted. We have also included the results of paired statistical tests (t-tests) on the key downstream benchmarks comparing Pile-trained models to the CC baselines. For the per-component Pile evaluations we now report standard deviations across model sizes. Due to the high computational cost of full-scale retraining we were limited in the number of replicate runs; however, the consistent direction and magnitude of gains across scales provide supporting evidence. The abstract and results section have been updated to reflect these additions. revision: partial

Circularity Check

0 steps flagged

No significant circularity; empirical dataset construction and comparisons are self-contained

full rationale

The paper constructs the Pile dataset from 22 subsets and reports empirical results showing improved performance of GPT-2/GPT-3 models trained on it versus Raw CC and CC-100 baselines on Pile components and downstream tasks. No derivation chain, equations, predictions, or first-principles results exist that could reduce to inputs by construction. The patterns of self-definitional claims, fitted inputs called predictions, self-citation load-bearing arguments, uniqueness theorems, ansatz smuggling, or renaming known results are absent. The central claims rest on new data assembly and direct comparisons against external benchmarks, with no self-referential reductions or load-bearing self-citations that collapse the argument.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The work is an empirical dataset construction and benchmarking effort; it introduces no mathematical free parameters, domain axioms, or postulated entities.

pith-pipeline@v0.9.0 · 5484 in / 1150 out tokens · 77761 ms · 2026-05-10T21:28:45.866889+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

193 extracted references · 193 canonical work pages · cited by 74 Pith papers · 11 internal anchors

  1. [1]

    Sony corp

    1984. Sony corp. of america v. universal city studios, inc

    work page 1984

  2. [2]

    2003. Kelly v. arriba soft corp

    work page 2003

  3. [3]

    Righthaven llc v

    2013. Righthaven llc v. hoehn

    work page 2013

  4. [5]

    Stefano Baccianella, Andrea Esuli, and Fabrizio Sebastiani. 2010. http://nmis.isti.cnr.it/sebastiani/Publications/LREC10.pdf Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining . In LREC. European Language Resources Association

    work page 2010

  5. [6]

    Emily M Bender and Batya Friedman. 2018. Data statements for natural language processing: Toward mitigating system bias and enabling better science. Transactions of the Association for Computational Linguistics, 6:587--604

    work page 2018

  6. [7]

    Stella Biderman. 2021. Data statement for the P ile. arXiv preprint arXiv

    work page 2021

  7. [8]

    Stella Biderman, Kieran Bicheno, and Leo Gao. 2021. Datasheet for the P ile. arXiv preprint arXiv

    work page 2021

  8. [9]

    Scheirer

    Stella Biderman and Walter J. Scheirer. 2020. Pitfalls in machine learning research: Reexamining the development cycle. NeurIPS ``I Can't Believe It's Not Better!'' Workshop

    work page 2020

  9. [10]

    David M Blei, Andrew Y Ng, and Michael I Jordan. 2003. Latent dirichlet allocation. Journal of machine Learning research, 3(Jan):993--1022

    work page 2003

  10. [12]

    Nick Bostrom. 2014. Superintelligence: Paths, Dangers, Strategies. Oxford University Press, Inc

    work page 2014

  11. [13]

    Nick Bostrom and Eliezer Yudkowsky. 2014. The ethics of artificial intelligence. The Cambridge handbook of artificial intelligence, 1:316--334

    work page 2014

  12. [16]

    Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, Alina Oprea, and Colin Raffel. 2020. http://arxiv.org/abs/2012.07805 Extracting training data from large language models

    work page arXiv 2020

  13. [18]

    Brian Christian. 2020. The Alignment Problem: Machine Learning and Human Values. WW Norton & Company

    work page 2020

  14. [19]

    Alina Maria Ciobanu, Liviu P Dinu, and Andrea Sgarro. 2017. Towards a map of the syntactic similarity of languages. In International Conference on Computational Linguistics and Intelligent Text Processing, pages 576--590. Springer

    work page 2017

  15. [20]

    Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzm \'a n, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2020. https://www.aclweb.org/anthology/2020.acl-main.747 Unsupervised cross-lingual representation learning at scale . In Proceedings of the 58th Annual Meeting of the Association for...

    work page 2020

  16. [22]

    Andrew Critch and David Krueger. 2020. AI Research Considerations for Human Existential Safety (ARCHES) . Preprint at acritch.com/arches http://acritch.com/arches

    work page 2020

  17. [24]

    Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT : Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North A merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) . Association for Com...

    work page 2019

  18. [25]

    István Endrédy and Attila Novák. 2013. More effective boilerplate removal – the GoldMiner algorithm. In Polibits

    work page 2013

  19. [26]

    Niels Ferguson and Bruce Schneier. 2003. Practical Cryptography. John Wiley & Sons

    work page 2003

  20. [27]

    Casey Fiesler, Nathan Beard, and Brian C Keegan. 2020. No robots, spiders, or scrapers: Legal and ethical regulation of data collection methods in social media terms of service. In Proceedings of the International AAAI Conference on Web and Social Media, volume 14, pages 187--196

    work page 2020

  21. [29]

    Aaron Gokaslan and Vanya Cohen. 2019. Openwebtext corpus. http://Skylion007.github.io/OpenWebTextCorpus

    work page 2019

  22. [30]

    Authors Guild v. Google. 2015. . Docket No. 13-4829-cv, 804:202

    work page 2015

  23. [31]

    Katja Grace, John Salvatier, Allan Dafoe, Baobao Zhang, and Owain Evans. 2018. When will AI exceed human performance? evidence from AI experts. Journal of Artificial Intelligence Research, 62:729--754

    work page 2018

  24. [32]

    David Graff, Junbo Kong, Ke Chen, and Kazuaki Maeda. 2003. English gigaword. Linguistic Data Consortium, Philadelphia, 4(1):34

    work page 2003

  25. [33]

    Declan Groves and Andy Way. 2006. Hybridity in mt: Experiments on the Europarl corpus. In Proceeedings of the 11th Annual conference of the European Association for Machine Translation (EAMT 2006)

    work page 2006

  26. [34]

    Alexander Halavais. 2019. Overcoming terms of service: a proposal for ethical distributed research. Information, Communication & Society, 22(11):1567--1581

    work page 2019

  27. [35]

    Chris Hardin. 2018. https://blog.janestreet.com/how-to-shuffle-a-big-dataset/ How to shuffle a big dataset

    work page 2018

  28. [37]

    Matthew Hoffman, Francis Bach, and David Blei. 2010. Online learning for latent dirichlet allocation. advances in neural information processing systems, 23:856--864

    work page 2010

  29. [38]

    Dirk Hovy and Shannon L Spruit. 2016. The social impact of natural language processing. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 591--598

    work page 2016

  30. [39]

    Ben Hutchinson, Vinodkumar Prabhakaran, Emily Denton, Kellie Webster, Yu Zhong, and Stephen Denuyl. 2020. Social biases in NLP models as barriers for persons with disabilities. arXiv preprint arXiv:2005.00813

    work page arXiv 2020

  31. [40]

    Eun Seo Jo and Timnit Gebru. 2020. Lessons from archives: S trategies for collecting sociocultural data in machine learning. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pages 306--316

    work page 2020

  32. [42]

    Bryan Klimt and Yiming Yang. 2004. The E nron corpus: A new dataset for email classification research. In European Conference on Machine Learning, pages 217--226. Springer

    work page 2004

  33. [43]

    Sosuke Kobayashi. 2018. Homemade bookcorpus. https://github.com/BIGBALLON/cifar-10-cnn

    work page 2018

  34. [44]

    Philipp Koehn. 2005. Europarl : A parallel corpus for statistical machine translation. In MT summit, volume 5, pages 79--86. Citeseer

    work page 2005

  35. [47]

    Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. RoBERTa : A robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692

    work page internal anchor Pith review Pith/arXiv arXiv 2019

  36. [48]

    Edward Loper and Steven Bird. 2002. NLTK: The Natural Language Toolkit . In Proceedings of the ACL Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics, pages 62--69. Somerset, NJ: Association for Computational Linguistics. http://arXiv.org/abs/cs/0205028

    work page internal anchor Pith review arXiv 2002

  37. [49]

    2006--2020

    John MacFarlane. 2006--2020. https://pandoc.org/ Pandoc: a universal document converter

    work page 2006

  38. [50]

    Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. https://proceedings.neurips.cc/paper/2013/file/9aa42b31882ec039965f3c4923ce901b-Paper.pdf Distributed representations of words and phrases and their compositionality . In Advances in Neural Information Processing Systems, volume 26, pages 3111--3119. Curran Associates, Inc

    work page 2013

  39. [51]

    Jonathan A Obar. 2020. Sunlight alone is not a disinfectant: Consent and the futility of opening big data black boxes (without assistance). Big Data & Society, 7(1):2053951720935615

    work page 2020

  40. [52]

    Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. http://www.aclweb.org/anthology/D14-1162 Glove: Global vectors for word representation . In Empirical Methods in Natural Language Processing (EMNLP), pages 1532--1543

    work page 2014

  41. [54]

    Shawn Presser. 2020. Books3. https://twitter.com/theshawwn/status/1320282149329784833

    work page arXiv 2020

  42. [55]

    Alec Radford, Karthik Narasimhan, Time Salimans, and Ilya Sutskever. 2018. Improving language understanding with unsupervised learning. Technical report, OpenAI

    work page 2018

  43. [56]

    Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners. OpenAI Blog, 1(8):9

    work page 2019

  44. [57]

    Jack W Rae, Anna Potapenko, Siddhant M Jayakumar, Chloe Hillier, and Timothy P Lillicrap. 2019. https://arxiv.org/abs/1911.05507 Compressive transformers for long-range sequence modelling . arXiv preprint

    work page arXiv 2019

  45. [59]

    Inioluwa Deborah Raji and Jingying Yang. 2019. ABOUT ML : Annotation and benchmarking on understanding and transparency of machine learning lifecycles. arXiv preprint arXiv:1912.06166

    work page arXiv 2019

  46. [60]

    Radhakrishna Rao

    C. Radhakrishna Rao. 1961. http://www.jstor.org/stable/25049166 Generation of random permutations of given number of elements using random sampling numbers . Sankhyā: The Indian Journal of Statistics, Series A (1961-2002), 23(3):305--307

    work page arXiv 1961

  47. [61]

    Radim Rehurek, Petr Sojka, et al. 2011. Gensim—statistical semantics in python. NLP Centre, Faculty of Informatics, Masaryk University

    work page 2011

  48. [62]

    C Rosset. 2019. Turing-NLG : A 17-billion-parameter language model by M icrosoft. Microsoft Blog

    work page 2019

  49. [63]

    S. Russell. 2019. https://books.google.de/books?id=M1eFDwAAQBAJ Human Compatible: Artificial Intelligence and the Problem of Control . Penguin Publishing Group

    work page 2019

  50. [66]

    Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, and Bryan Catanzaro. 2019. Megatron-LM : Training multi-billion parameter language models using gpu model parallelism. arXiv preprint arXiv:1909.08053

    work page internal anchor Pith review Pith/arXiv arXiv 2019

  51. [67]

    Carl Shulman and Nick Bostrom. 2020. Sharing the world with digital minds. preprint

    work page 2020

  52. [68]

    Kaj Sotala and Lukas Gloor. 2017. Superintelligence as a cause or cure for risks of astronomical suffering. Informatica, 41(4)

    work page 2017

  53. [70]

    Merity Stephen, Xiong Caiming, Bradbury James, and Richard Socher. 2016

    work page 2016

  54. [71]

    Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F Christiano. 2020. Learning to summarize with human feedback. Advances in Neural Information Processing Systems, 33

    work page 2020

  55. [72]

    Pedro Javier Ortiz Su \'a rez, Beno \^ t Sagot, and Laurent Romary. 2019 a . Asynchronous pipeline for processing huge corpora on medium to low resource infrastructures. In 7th Workshop on the Challenges in the Management of Large Corpora (CMLC-7). Leibniz-Institut f \"u r Deutsche Sprache

    work page 2019

  56. [73]

    Pedro Javier Ortiz Su \'a rez, Beno \^ t Sagot, and Laurent Romary. 2019 b . Asynchronous pipeline for processing huge corpora on medium to low resource infrastructures. In 7th Workshop on the Challenges in the Management of Large Corpora (CMLC-7). Leibniz-Institut f \"u r Deutsche Sprache

    work page 2019

  57. [74]

    Anja Thieme, Danielle Belgrave, and Gavin Doherty. 2020. Machine learning in mental health: A systematic review of the HCI literature to support the development of effective and implementable ML systems. ACM Transactions on Computer-Human Interaction (TOCHI), 27(5):1--53

    work page 2020

  58. [75]

    Tiedemann

    J. Tiedemann. 2016. Finding alternative translations in a large corpus of movie subtitles. Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 2016)

    work page 2016

  59. [76]

    A simple method for commonsense reasoning

    Trieu H. Trinh and Quoc V. Le. 2018. http://arxiv.org/abs/1806.02847 A simple method for commonsense reasoning . CoRR, abs/1806.02847

    work page arXiv 2018

  60. [77]

    Hans Van Halteren. 2008. Source language markers in Europarl translations. In Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), pages 937--944

    work page 2008

  61. [78]

    Jessica Vitak, Katie Shilton, and Zahra Ashktorab. 2016. Beyond the Belmont principles: Ethical challenges, practices, and beliefs in the online data research community. In Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing, pages 941--953

    work page 2016

  62. [80]

    Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2020. https://www.aclweb.org/a...

    work page 2020

  63. [82]

    Eliezer Yudkowsky. 2013. Intelligence explosion microeconomics. Machine Intelligence Research Institute, accessed online October, 23:2015

    work page 2013

  64. [83]

    Rowan Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi, Franziska Roesner, and Yejin Choi. 2019. http://papers.nips.cc/paper/9106-defending-against-neural-fake-news.pdf Defending against neural fake news . In H. Wallach, H. Larochelle, A. Beygelzimer, F. d\' Alch\' e -Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Pro...

    work page 2019

  65. [84]

    Victor Zhou. 2019. Building a better profanity detection library with scikit-learn

    work page 2019

  66. [85]

    Yukun Zhu, Ryan Kiros, Rich Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015. Aligning books and movies: Towards story-like visual explanations by watching movies and reading books. In Proceedings of the IEEE international conference on computer vision, pages 19--27

    work page 2015

  67. [86]

    Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and Presser, Shawn and Leahy, Connor , journal=. The

    work page

  68. [87]

    Datasheet for the

    Biderman, Stella and Bicheno, Kieran and Gao, Leo , journal=. Datasheet for the

    work page

  69. [88]

    Data Statement for the

    Biderman, Stella , journal=. Data Statement for the

    work page

  70. [89]

    OpenAI Blog , volume=

    Language models are unsupervised multitask learners , author=. OpenAI Blog , volume=

    work page

  71. [90]

    Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

    Exploring the limits of transfer learning with a unified text-to-text transformer , author=. arXiv preprint arXiv:1910.10683 , year=

    work page internal anchor Pith review arXiv 1910

  72. [91]

    Shoeybi, Mohammad and Patwary, Mostofa and Puri, Raul and LeGresley, Patrick and Casper, Jared and Catanzaro, Bryan , journal=

    work page

  73. [92]

    Rosset, C , journal=

    work page

  74. [93]

    Language Models are Few-Shot Learners

    Language models are few-shot learners , author=. arXiv preprint arXiv:2005.14165 , year=

    work page internal anchor Pith review Pith/arXiv arXiv 2005

  75. [94]

    GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding

    Gshard: Scaling giant models with conditional computation and automatic sharding , author=. arXiv preprint arXiv:2006.16668 , year=

    work page internal anchor Pith review arXiv 2006

  76. [95]

    Technical report, OpenAI , year=

    Improving language understanding with unsupervised learning , author=. Technical report, OpenAI , year=

    work page

  77. [96]

    BERT : Pre-training of Deep Bidirectional Transformers for Language Understanding

    Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina. BERT : Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the 2019 Conference of the North A merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019

    work page 2019

  78. [97]

    Yinhan Liu and Myle Ott and Naman Goyal and Jingfei Du and Mandar Joshi and Danqi Chen and Omer Levy and Mike Lewis and Luke Zettlemoyer and Veselin Stoyanov , journal=

    work page

  79. [98]

    KONVENS , year=

    Generic Web Content Extraction with Open-Source Software , author=. KONVENS , year=

    work page

  80. [99]

    Polibits , year =

    István Endrédy and Attila Novák , title =. Polibits , year =

    work page

Showing first 80 references.