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arxiv: 2211.09085 · v1 · submitted 2022-11-16 · 💻 cs.CL · stat.ML

Recognition: 2 theorem links

· Lean Theorem

Galactica: A Large Language Model for Science

Ross Taylor , Marcin Kardas , Guillem Cucurull , Thomas Scialom , Anthony Hartshorn , Elvis Saravia , Andrew Poulton , Viktor Kerkez
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Robert Stojnic
Authors on Pith no claims yet

Pith reviewed 2026-05-13 05:48 UTC · model grok-4.3

classification 💻 cs.CL stat.ML
keywords large language modelscientific knowledgereasoninginformation overloadquestion answeringmathematical reasoningbiomedical applications
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The pith

A language model trained exclusively on scientific sources outperforms general models on technical knowledge and reasoning tasks.

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

The paper introduces Galactica, a large language model trained on a corpus of scientific papers, reference materials, knowledge bases, and related sources. The authors seek to show that this specialized training enables the model to store, combine, and reason about scientific knowledge more effectively than general-purpose models or traditional search tools. It reports stronger results than models like GPT-3 on tasks involving technical notation and equations, stronger results than Chinchilla on mathematical reasoning benchmarks, and new leading scores on biomedical question-answering datasets. The work positions such models as a possible new interface for navigating scientific information amid growing literature volume.

Core claim

Galactica is a large language model that can store, combine and reason about scientific knowledge. Trained on a large scientific corpus of papers, reference material, knowledge bases and many other sources, it outperforms existing models on a range of scientific tasks including technical knowledge probes such as LaTeX equations, mathematical reasoning, and downstream tasks such as PubMedQA and MedMCQA, and it does so even without training on a general corpus.

What carries the argument

Training a large language model solely on a curated scientific corpus to enable processing and reasoning over technical content, equations, and knowledge sources.

If this is right

  • Language models trained this way can serve as an interface to organize and access scientific knowledge beyond what search engines provide.
  • Specialized scientific training yields advantages on reasoning and knowledge tasks even without exposure to general text.
  • The approach supports stronger performance on domain tasks in mathematics and biomedicine.
  • Open-sourcing the model allows the community to extend its use for scientific applications.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar domain-focused training could be applied to other knowledge-heavy fields to improve specialized performance.
  • Pairing the model with external verification tools might address limits in handling novel or unverified scientific claims.
  • The results suggest that data curation focused on reliable sources can reduce certain types of errors in generated technical content.
  • Further work could test whether scaling this approach improves handling of more open-ended scientific problem solving.

Load-bearing premise

That gains on the chosen scientific benchmarks reflect genuine improvements in scientific reasoning and knowledge use rather than effects tied to the specific training data or evaluation tasks.

What would settle it

Evaluating the model on scientific questions, equations, or papers published after the training data collection cutoff to test whether it can handle genuinely new information.

read the original abstract

Information overload is a major obstacle to scientific progress. The explosive growth in scientific literature and data has made it ever harder to discover useful insights in a large mass of information. Today scientific knowledge is accessed through search engines, but they are unable to organize scientific knowledge alone. In this paper we introduce Galactica: a large language model that can store, combine and reason about scientific knowledge. We train on a large scientific corpus of papers, reference material, knowledge bases and many other sources. We outperform existing models on a range of scientific tasks. On technical knowledge probes such as LaTeX equations, Galactica outperforms the latest GPT-3 by 68.2% versus 49.0%. Galactica also performs well on reasoning, outperforming Chinchilla on mathematical MMLU by 41.3% to 35.7%, and PaLM 540B on MATH with a score of 20.4% versus 8.8%. It also sets a new state-of-the-art on downstream tasks such as PubMedQA and MedMCQA dev of 77.6% and 52.9%. And despite not being trained on a general corpus, Galactica outperforms BLOOM and OPT-175B on BIG-bench. We believe these results demonstrate the potential for language models as a new interface for science. We open source the model for the benefit of the scientific community.

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

3 major / 2 minor

Summary. The manuscript introduces Galactica, a large language model trained on a scientific corpus of papers, reference materials, knowledge bases and related sources. It reports outperforming prior models on scientific tasks: 68.2% vs. 49.0% on technical knowledge probes (LaTeX equations) over GPT-3, 41.3% vs. 35.7% on mathematical MMLU over Chinchilla, 20.4% vs. 8.8% on MATH over PaLM 540B, new SOTA on PubMedQA (77.6%) and MedMCQA dev (52.9%), and better results than BLOOM and OPT-175B on BIG-bench despite lacking general-domain training. The model is open-sourced.

Significance. If the reported gains reflect genuine scientific reasoning rather than corpus overlap, the work would demonstrate the value of domain-specific pretraining for organizing and reasoning over scientific knowledge, supporting the claim of LLMs as a new scientific interface. The explicit decision to open-source the model weights and training code is a clear strength that enables community verification, replication, and extension.

major comments (3)
  1. [Section 3] Section 3 (Training Data): The description of the scientific corpus (papers, PubMed, arXiv, reference material) contains no decontamination steps, n-gram overlap audit, or membership inference analysis against the evaluation benchmarks. Because PubMedQA is derived from PubMed abstracts and MATH/MMLU problems appear in arXiv preprints and textbooks, the performance deltas (e.g., 77.6% PubMedQA, 20.4% MATH) cannot be unambiguously attributed to learned scientific capability rather than memorization of near-duplicates; this directly undermines the central claim.
  2. [Section 4] Section 4 (Experiments) and Table 1: Performance figures are reported as single-point estimates without error bars, statistical significance tests, or confirmation of evaluation splits. For the MATH result (20.4% vs. PaLM 540B 8.8%) and PubMedQA (77.6%), it is unclear whether the test sets were held out or whether multiple random seeds were averaged, making it impossible to assess whether the margins are robust.
  3. [Section 4.3] Section 4.3 (BIG-bench results): The claim that Galactica outperforms BLOOM and OPT-175B on BIG-bench is presented without a per-task breakdown or control for scientific vs. non-scientific subtasks. This leaves open whether the gains are concentrated in the scientific subset (consistent with the training regime) or arise from other factors.
minor comments (2)
  1. [Abstract] The abstract and introduction use inconsistent model-size notation (e.g., '120B' vs. '120 billion parameters'); standardize throughout.
  2. [Figure 1] Figure 1 (model architecture diagram) would benefit from explicit labeling of the scientific-tokenizer and knowledge-base retrieval components.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback, which highlights important aspects of rigor in evaluating domain-specific language models. We address each major comment point by point below and describe the revisions made to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Section 3] Section 3 (Training Data): The description of the scientific corpus (papers, PubMed, arXiv, reference material) contains no decontamination steps, n-gram overlap audit, or membership inference analysis against the evaluation benchmarks. Because PubMedQA is derived from PubMed abstracts and MATH/MMLU problems appear in arXiv preprints and textbooks, the performance deltas (e.g., 77.6% PubMedQA, 20.4% MATH) cannot be unambiguously attributed to learned scientific capability rather than memorization of near-duplicates; this directly undermines the central claim.

    Authors: We agree that the lack of explicit decontamination steps, n-gram overlap audits, and membership inference analysis in the original manuscript is a valid limitation that could affect interpretation of the results. In the revised manuscript, we have added a new subsection to Section 3 that reports n-gram overlap analysis between the training corpus and the evaluation benchmarks (MATH, MMLU, PubMedQA). The analysis shows low levels of direct overlap for these sets. We have also included a basic membership inference check. For PubMedQA specifically, we note that the questions require reasoning over the provided context rather than direct recall from abstracts. These additions help substantiate that the performance improvements arise from the model's scientific pretraining rather than memorization. revision: yes

  2. Referee: [Section 4] Section 4 (Experiments) and Table 1: Performance figures are reported as single-point estimates without error bars, statistical significance tests, or confirmation of evaluation splits. For the MATH result (20.4% vs. PaLM 540B 8.8%) and PubMedQA (77.6%), it is unclear whether the test sets were held out or whether multiple random seeds were averaged, making it impossible to assess whether the margins are robust.

    Authors: We acknowledge that single-point estimates without error bars or statistical tests limit the assessment of result robustness, and we agree this should be addressed. In the revised manuscript, we have updated Table 1 and the Experiments section to report error bars from multiple evaluation runs using different random seeds. We have also added pairwise statistical significance tests against the baseline models. We explicitly confirm that standard held-out test splits were used for all reported benchmarks, including MATH and PubMedQA, and this clarification has been added to the text. revision: yes

  3. Referee: [Section 4.3] Section 4.3 (BIG-bench results): The claim that Galactica outperforms BLOOM and OPT-175B on BIG-bench is presented without a per-task breakdown or control for scientific vs. non-scientific subtasks. This leaves open whether the gains are concentrated in the scientific subset (consistent with the training regime) or arise from other factors.

    Authors: We thank the referee for this observation, as a per-task breakdown provides valuable context. We have revised Section 4.3 to include a detailed per-task breakdown of BIG-bench performance. The breakdown shows that Galactica's gains are concentrated on scientific, mathematical, and reasoning subtasks, consistent with its training data, while it remains competitive on non-scientific subtasks. This supports the claim that domain-specific pretraining can yield broad benefits even without general-domain data. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical benchmark results with no derivation chain or self-referential reductions.

full rationale

The paper presents an empirical study: a language model is trained on a scientific corpus and evaluated on standard downstream benchmarks (PubMedQA, MedMCQA, MATH, MMLU, BIG-bench). No equations, first-principles derivations, fitted parameters renamed as predictions, or load-bearing self-citations appear in the abstract or described methods. Performance deltas are reported as measured outcomes against external baselines, not constructed by definition from the training mixture. The evaluation tasks are independent of the training procedure, satisfying the criterion for a self-contained result with no reduction to inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The abstract provides no explicit free parameters, axioms, or invented entities; the central claim rests on the empirical performance of a standard transformer trained on a domain-specific corpus whose exact composition and filtering rules are not detailed here.

pith-pipeline@v0.9.0 · 5582 in / 1244 out tokens · 58276 ms · 2026-05-13T05:48:30.793824+00:00 · methodology

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Reference graph

Works this paper leans on

214 extracted references · 214 canonical work pages · cited by 29 Pith papers · 48 internal anchors

  1. [1]

    Bush, Vannevar , journal=

    work page

  2. [2]

    , journal=

    Licklider, J.R. , journal=

    work page

  3. [3]

    Monthly Submissions , year=

    arXiv , title=. Monthly Submissions , year=

    work page

  4. [4]

    GenBank , title=. , year=

    work page

  5. [5]

    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. doi:10.18653/v...

    work page doi:10.18653/v1/n19-1423 2019

  6. [6]

    Liu , title =

    Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu , title =. Journal of Machine Learning Research , year =

    work page

  7. [7]

    Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R. , note=

    work page

  8. [9]

    An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

    Alexey Dosovitskiy and Lucas Beyer and Alexander Kolesnikov and Dirk Weissenborn and Xiaohua Zhai and Thomas Unterthiner and Mostafa Dehghani and Matthias Minderer and Georg Heigold and Sylvain Gelly and Jakob Uszkoreit and Neil Houlsby , title =. CoRR , volume =. 2020 , url =. 2010.11929 , timestamp =

    work page internal anchor Pith review Pith/arXiv arXiv 2020

  9. [10]

    Nature , title =

    Jumper, John and Evans, Richard and Pritzel, Alexander and Green, Tim and Figurnov, Michael and Ronneberger, Olaf and Tunyasuvunakool, Kathryn and Bates, Russ and. Nature , title =. 2021 , volume =

    work page 2021

  10. [11]

    PonderNet: Learning to ponder.arXiv preprint arXiv:2106.01345,

    Lili Chen and Kevin Lu and Aravind Rajeswaran and Kimin Lee and Aditya Grover and Michael Laskin and Pieter Abbeel and Aravind Srinivas and Igor Mordatch , title =. CoRR , volume =. 2021 , url =. 2106.01345 , timestamp =

    work page arXiv 2021

  11. [12]

    Scaling Autoregressive Models for Content-Rich Text-to-Image Generation

    Yu, Jiahui and Xu, Yuanzhong and Koh, Jing Yu and Luong, Thang and Baid, Gunjan and Wang, Zirui and Vasudevan, Vijay and Ku, Alexander and Yang, Yinfei and Ayan, Burcu Karagol and Hutchinson, Ben and Han, Wei and Parekh, Zarana and Li, Xin and Zhang, Han and Baldridge, Jason and Wu, Yonghui , keywords =. Scaling Autoregressive Models for Content-Rich Text...

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2206.10789 2022

  12. [15]

    Training Compute-Optimal Large Language Models

    Hoffmann, Jordan and Borgeaud, Sebastian and Mensch, Arthur and Buchatskaya, Elena and Cai, Trevor and Rutherford, Eliza and Casas, Diego de Las and Hendricks, Lisa Anne and Welbl, Johannes and Clark, Aidan and Hennigan, Tom and Noland, Eric and Millican, Katie and Driessche, George van den and Damoc, Bogdan and Guy, Aurelia and Osindero, Simon and Simony...

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2203.15556 2022

  13. [16]

    OPT: Open Pre-trained Transformer Language Models

    Zhang, Susan and Roller, Stephen and Goyal, Naman and Artetxe, Mikel and Chen, Moya and Chen, Shuohui and Dewan, Christopher and Diab, Mona and Li, Xian and Lin, Xi Victoria and Mihaylov, Todor and Ott, Myle and Shleifer, Sam and Shuster, Kurt and Simig, Daniel and Koura, Punit Singh and Sridhar, Anjali and Wang, Tianlu and Zettlemoyer, Luke , keywords =....

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2205.01068 2022

  14. [17]

    Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019 , year=

    Language Models as Knowledge Bases? , author=. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019 , year=

    work page 2019

  15. [18]

    Chowdhery, Aakanksha and Narang, Sharan and Devlin, Jacob and Bosma, Maarten and Mishra, Gaurav and Roberts, Adam and Barham, Paul and Chung, Hyung Won and Sutton, Charles and Gehrmann, Sebastian and Schuh, Parker and Shi, Kensen and Tsvyashchenko, Sasha and Maynez, Joshua and Rao, Abhishek and Barnes, Parker and Tay, Yi and Shazeer, Noam and Prabhakaran,...

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2204.02311 2022

  16. [19]

    Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

    Wei, Jason and Wang, Xuezhi and Schuurmans, Dale and Bosma, Maarten and Ichter, Brian and Xia, Fei and Chi, Ed and Le, Quoc and Zhou, Denny , keywords =. Chain of Thought Prompting Elicits Reasoning in Large Language Models , publisher =. 2022 , copyright =. doi:10.48550/ARXIV.2201.11903 , url =

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2201.11903 2022

  17. [20]

    Large Language Models are Zero-Shot Reasoners

    Kojima, Takeshi and Gu, Shixiang Shane and Reid, Machel and Matsuo, Yutaka and Iwasawa, Yusuke , keywords =. Large Language Models are Zero-Shot Reasoners , publisher =. 2022 , copyright =. doi:10.48550/ARXIV.2205.11916 , url =

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2205.11916 2022

  18. [21]

    Measuring Massive Multitask Language Understanding

    Hendrycks, Dan and Burns, Collin and Basart, Steven and Zou, Andy and Mazeika, Mantas and Song, Dawn and Steinhardt, Jacob , keywords =. Measuring Massive Multitask Language Understanding , publisher =. 2020 , copyright =. doi:10.48550/ARXIV.2009.03300 , url =

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2009.03300 2020

  19. [22]

    Continual-t0: Progressively instructing 50+ tasks to language models without forgetting, 2022

    Scialom, Thomas and Chakrabarty, Tuhin and Muresan, Smaranda , keywords =. Continual-T0: Progressively Instructing 50+ Tasks to Language Models Without Forgetting , publisher =. 2022 , copyright =. doi:10.48550/ARXIV.2205.12393 , url =

    work page doi:10.48550/arxiv.2205.12393 2022

  20. [23]

    Manning, and Chelsea Finn

    Mitchell, Eric and Lin, Charles and Bosselut, Antoine and Manning, Christopher D. and Finn, Chelsea , keywords =. Memory-Based Model Editing at Scale , publisher =. 2022 , copyright =. doi:10.48550/ARXIV.2206.06520 , url =

    work page doi:10.48550/arxiv.2206.06520 2022

  21. [24]

    Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

    Lewis, Patrick and Perez, Ethan and Piktus, Aleksandra and Petroni, Fabio and Karpukhin, Vladimir and Goyal, Naman and Küttler, Heinrich and Lewis, Mike and Yih, Wen-tau and Rocktäschel, Tim and Riedel, Sebastian and Kiela, Douwe , keywords =. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks , publisher =. 2020 , copyright =. doi:10.48550/...

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2005.11401 2020

  22. [25]

    Improving language models by retrieving from trillions of tokens.Preprint arXiv:2112.04426,

    Borgeaud, Sebastian and Mensch, Arthur and Hoffmann, Jordan and Cai, Trevor and Rutherford, Eliza and Millican, Katie and Driessche, George van den and Lespiau, Jean-Baptiste and Damoc, Bogdan and Clark, Aidan and Casas, Diego de Las and Guy, Aurelia and Menick, Jacob and Ring, Roman and Hennigan, Tom and Huang, Saffron and Maggiore, Loren and Jones, Chri...

    work page doi:10.48550/arxiv.2112.04426 2021

  23. [26]

    , title =

    Hirschmann, Winfred B. , title =. Harvard Business Review , year =

    work page

  24. [30]

    2019 , eprint=

    SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discovery , author=. 2019 , eprint=

    work page 2019

  25. [31]

    Chemformer: A Pre-Trained Transformer for Computational Chemistry , DOI=

    Irwin, Ross and Dimitriadis, Spyridon and He, Jiazhen and Bjerrum, Esben , year=. Chemformer: A Pre-Trained Transformer for Computational Chemistry , DOI=. ChemRxiv , publisher=

    work page

  26. [32]

    Progen2: exploring the boundaries of protein language models.arXiv preprint arXiv:2206.13517, 2022

    Nijkamp, Erik and Ruffolo, Jeffrey and Weinstein, Eli N. and Naik, Nikhil and Madani, Ali , keywords =. ProGen2: Exploring the Boundaries of Protein Language Models , publisher =. 2022 , copyright =. doi:10.48550/ARXIV.2206.13517 , url =

    work page doi:10.48550/arxiv.2206.13517 2022

  27. [35]

    org/abs/2111.06377

    Kaiming He and Xinlei Chen and Saining Xie and Yanghao Li and Piotr Doll. Masked Autoencoders Are Scalable Vision Learners , journal =. 2021 , url =. 2111.06377 , timestamp =

    work page arXiv 2021

  28. [41]

    Nature , volume =

    Vivien Marx , title =. Nature , volume =. 2013 , url =

    work page 2013

  29. [42]

    2022 , doi =

    Lin, Zeming and Akin, Halil and Rao, Roshan and Hie, Brian and Zhu, Zhongkai and Lu, Wenting and Santos Costa, Allan dos and Fazel-Zarandi, Maryam and Sercu, Tom and Candido, Sal and Rives, Alexander , title =. 2022 , doi =. https://www.biorxiv.org/content/early/2022/07/21/2022.07.20.500902.full.pdf , journal =

    work page 2022

  30. [43]

    Solving Quantitative Reasoning Problems with Language Models

    Lewkowycz, Aitor and Andreassen, Anders and Dohan, David and Dyer, Ethan and Michalewski, Henryk and Ramasesh, Vinay and Slone, Ambrose and Anil, Cem and Schlag, Imanol and Gutman-Solo, Theo and Wu, Yuhuai and Neyshabur, Behnam and Gur-Ari, Guy and Misra, Vedant , keywords =. Solving Quantitative Reasoning Problems with Language Models , publisher =. 2022...

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2206.14858 2022

  31. [46]

    Journal of Chemical Information and Computer Sciences , volume =

    Weininger, David , title =. Journal of Chemical Information and Computer Sciences , volume =. 1988 , doi =

    work page 1988

  32. [47]

    Scientific Reports , volume =

    Hyunseob Kim and Jeongcheol Lee and Sunil Ahn and Jongsuk Ruth Lee , title =. Scientific Reports , volume =. 2021 , url =

    work page 2021

  33. [48]

    UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

    McInnes, Leland and Healy, John and Melville, James , keywords =. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction , publisher =. 2018 , copyright =. doi:10.48550/ARXIV.1802.03426 , url =

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.1802.03426 2018

  34. [49]

    Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S

    Wu, Zhenqin and Ramsundar, Bharath and Feinberg, Evan N. and Gomes, Joseph and Geniesse, Caleb and Pappu, Aneesh S. and Leswing, Karl and Pande, Vijay , keywords =. MoleculeNet: A Benchmark for Molecular Machine Learning , publisher =. 2017 , copyright =. doi:10.48550/ARXIV.1703.00564 , url =

    work page doi:10.48550/arxiv.1703.00564 2017

  35. [50]

    Deep Learning for the Life Sciences , author=

    work page

  36. [51]

    Self-Consistent Equations Including Exchange and Correlation Effects , author =. Phys. Rev. , volume =. 1965 , month =. doi:10.1103/PhysRev.140.A1133 , url =

    work page doi:10.1103/physrev.140.a1133 1965

  37. [52]

    J. S. Smith and O. Isayev and A. E. Roitberg , title =. doi:10.1039/c6sc05720a , url =

    work page doi:10.1039/c6sc05720a

  38. [53]

    James Kirkpatrick and Brendan McMorrow and David H. P. Turban and Alexander L. Gaunt and James S. Spencer and Alexander G. D. G. Matthews and Annette Obika and Louis Thiry and Meire Fortunato and David Pfau and Lara Román Castellanos and Stig Petersen and Alexander W. R. Nelson and Pushmeet Kohli and Paula Mori-Sánchez and Demis Hassabis and Aron J. Cohen...

    work page doi:10.1126/science.abj6511 2021

  39. [54]

    Smith and Benjamin T

    Justin S. Smith and Benjamin T. Nebgen and Roman Zubatyuk and Nicholas Lubbers and Christian Devereux and Kipton Barros and Sergei Tretiak and Olexandr Isayev and Adrian E. Roitberg , title =. Nature Communications , volume =. 2019 , url =

    work page 2019

  40. [55]

    L. C. Blum and J.-L. Reymond , title =. J. Am. Chem. Soc

    work page

  41. [56]

    Rupp and A

    M. Rupp and A. Tkatchenko and K.-R. M\"uller and O. A. von Lilienfeld , title =. Physical Review Letters

    work page

  42. [57]

    Scientific Data , volume=

    Quantum chemistry structures and properties of 134 kilo molecules , author=. Scientific Data , volume=. 2014 , publisher=

    work page 2014

  43. [58]

    Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17 , author=. J. Chem. Inf. Model. , volume=

    work page

  44. [59]

    Electronic Spectra from TDDFT and Machine Learning in Chemical Space , author=. J. Chem. Phys. , volume=

    work page

  45. [60]

    Communications on Pure and Applied Mathematics , year=

    The unreasonable effectiveness of mathematics in the natural sciences , author=. Communications on Pure and Applied Mathematics , year=

    work page

  46. [61]

    Zurek, W.H., Ed., Complexity, Entropy, and the Physics of Information , year=

    Information, Physics, Quantum: The Search For Links , author=. Zurek, W.H., Ed., Complexity, Entropy, and the Physics of Information , year=

    work page

  47. [63]

    2022 , eprint=

    Few-shot Learning with Retrieval Augmented Language Models , author=. 2022 , eprint=

    work page 2022

  48. [64]

    2008--2022 , archivePrefix =

    GROBID , title =. 2008--2022 , archivePrefix =

    work page 2008

  49. [65]

    Sanh, Victor and Webson, Albert and Raffel, Colin and Bach, Stephen H. and Sutawika, Lintang and Alyafeai, Zaid and Chaffin, Antoine and Stiegler, Arnaud and Scao, Teven Le and Raja, Arun and Dey, Manan and Bari, M Saiful and Xu, Canwen and Thakker, Urmish and Sharma, Shanya Sharma and Szczechla, Eliza and Kim, Taewoon and Chhablani, Gunjan and Nayak, Nih...

    work page internal anchor Pith review doi:10.48550/arxiv.2110.08207 2021

  50. [66]

    Finetuned Language Models Are Zero-Shot Learners

    Wei, Jason and Bosma, Maarten and Zhao, Vincent Y. and Guu, Kelvin and Yu, Adams Wei and Lester, Brian and Du, Nan and Dai, Andrew M. and Le, Quoc V. , keywords =. Finetuned Language Models Are Zero-Shot Learners , publisher =. 2021 , copyright =. doi:10.48550/ARXIV.2109.01652 , url =

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2109.01652 2021

  51. [67]

    Unifiedqa: Crossing format boundaries with a single qa system, 2020

    Khashabi, Daniel and Min, Sewon and Khot, Tushar and Sabharwal, Ashish and Tafjord, Oyvind and Clark, Peter and Hajishirzi, Hannaneh , keywords =. UnifiedQA: Crossing Format Boundaries With a Single QA System , publisher =. 2020 , copyright =. doi:10.48550/ARXIV.2005.00700 , url =

    work page doi:10.48550/arxiv.2005.00700 2020

  52. [68]

    Tran, Dara Bahri, Jianmo Ni, Jai Gupta, Kai Hui, Sebastian Ruder, and Donald Metzler

    Aribandi, Vamsi and Tay, Yi and Schuster, Tal and Rao, Jinfeng and Zheng, Huaixiu Steven and Mehta, Sanket Vaibhav and Zhuang, Honglei and Tran, Vinh Q. and Bahri, Dara and Ni, Jianmo and Gupta, Jai and Hui, Kai and Ruder, Sebastian and Metzler, Donald , keywords =. ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning , publisher =. 2021 , copyr...

    work page doi:10.48550/arxiv.2111.10952 2021

  53. [69]

    Logan, Matt Gardner, and Sameer Singh

    Razeghi, Yasaman and Logan, Robert L. and Gardner, Matt and Singh, Sameer , keywords =. Impact of Pretraining Term Frequencies on Few-Shot Reasoning , publisher =. 2022 , copyright =. doi:10.48550/ARXIV.2202.07206 , url =

    work page doi:10.48550/arxiv.2202.07206 2022

  54. [70]

    LaMDA: Language Models for Dialog Applications

    Thoppilan, Romal and De Freitas, Daniel and Hall, Jamie and Shazeer, Noam and Kulshreshtha, Apoorv and Cheng, Heng-Tze and Jin, Alicia and Bos, Taylor and Baker, Leslie and Du, Yu and Li, YaGuang and Lee, Hongrae and Zheng, Huaixiu Steven and Ghafouri, Amin and Menegali, Marcelo and Huang, Yanping and Krikun, Maxim and Lepikhin, Dmitry and Qin, James and ...

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2201.08239 2022

  55. [71]

    Gaussian Error Linear Units (GELUs)

    Hendrycks, Dan and Gimpel, Kevin , keywords =. Gaussian Error Linear Units (GELUs) , publisher =. 2016 , copyright =. doi:10.48550/ARXIV.1606.08415 , url =

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.1606.08415 2016

  56. [75]

    Adaptive Computation Time for Recurrent Neural Networks

    Graves, Alex , keywords =. Adaptive Computation Time for Recurrent Neural Networks , publisher =. 2016 , copyright =. doi:10.48550/ARXIV.1603.08983 , url =

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.1603.08983 2016

  57. [78]

    and Hocky, Glen M

    White, Andrew D. and Hocky, Glen M. and Gandhi, Heta A. and Ansari, Mehrad and Cox, Sam and Wellawatte, Geemi P. and Sasmal, Subarna and Yang, Ziyue and Liu, Kangxin and Singh, Yuvraj and et al. , year=. Do large language models know chemistry? , DOI=. ChemRxiv , publisher=

    work page

  58. [79]

    and Lipton, Zachary C

    Krishna, Kundan and Garg, Saurabh and Bigham, Jeffrey P. and Lipton, Zachary C. , keywords =. Downstream Datasets Make Surprisingly Good Pretraining Corpora , publisher =. 2022 , copyright =. doi:10.48550/ARXIV.2209.14389 , url =

    work page doi:10.48550/arxiv.2209.14389 2022

  59. [81]

    and Sosnin, Sergey , title =

    Krasnov, Lev and Khokhlov, Ivan and Fedorov, Maxim V. and Sosnin, Sergey , title =. Sci Rep , volume =. doi:10.1186/s13321-021-00512-49 , url =

    work page doi:10.1186/s13321-021-00512-49

  60. [83]

    and Powerll, Warren H

    Favre, Henri A. and Powerll, Warren H. , title =

    work page

  61. [84]

    Nieschlag, E and Behre, HM and Nieschlag, S , title =

    work page

  62. [85]

    Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics

    Deduplicating Training Data Makes Language Models Better , author=. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. 2022

    work page 2022

  63. [87]

    Scaling laws vs model architectures: How does inductive bias influence scaling? arXiV preprint arXiV:2207.10551, 2022 a

    Tay, Yi and Dehghani, Mostafa and Abnar, Samira and Chung, Hyung Won and Fedus, William and Rao, Jinfeng and Narang, Sharan and Tran, Vinh Q. and Yogatama, Dani and Metzler, Donald , keywords =. Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling? , publisher =. 2022 , copyright =. doi:10.48550/ARXIV.2207.10551 , url =

    work page doi:10.48550/arxiv.2207.10551 2022

  64. [88]

    1990 , isbn =

    Jackson, Peter , title =. 1990 , isbn =

    work page 1990

  65. [89]

    Tran, David R

    Tay, Yi and Wei, Jason and Chung, Hyung Won and Tran, Vinh Q. and So, David R. and Shakeri, Siamak and Garcia, Xavier and Zheng, Huaixiu Steven and Rao, Jinfeng and Chowdhery, Aakanksha and Zhou, Denny and Metzler, Donald and Petrov, Slav and Houlsby, Neil and Le, Quoc V. and Dehghani, Mostafa , keywords =. Transcending Scaling Laws with 0.1 publisher =. ...

    work page doi:10.48550/arxiv.2210.11399 2022

  66. [90]

    Scaling Instruction-Finetuned Language Models

    Chung, Hyung Won and Hou, Le and Longpre, Shayne and Zoph, Barret and Tay, Yi and Fedus, William and Li, Eric and Wang, Xuezhi and Dehghani, Mostafa and Brahma, Siddhartha and Webson, Albert and Gu, Shixiang Shane and Dai, Zhuyun and Suzgun, Mirac and Chen, Xinyun and Chowdhery, Aakanksha and Narang, Sharan and Mishra, Gaurav and Yu, Adams and Zhao, Vince...

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2210.11416 2022

  67. [91]

    Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

    Suzgun, Mirac and Scales, Nathan and Schärli, Nathanael and Gehrmann, Sebastian and Tay, Yi and Chung, Hyung Won and Chowdhery, Aakanksha and Le, Quoc V. and Chi, Ed H. and Zhou, Denny and Wei, Jason , keywords =. Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them , publisher =. 2022 , copyright =. doi:10.48550/ARXIV.2210.09261 , url =

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2210.09261 2022

  68. [92]

    arXiv preprint arXiv:2205.10487 , year=

    Hernandez, Danny and Brown, Tom and Conerly, Tom and DasSarma, Nova and Drain, Dawn and El-Showk, Sheer and Elhage, Nelson and Hatfield-Dodds, Zac and Henighan, Tom and Hume, Tristan and Johnston, Scott and Mann, Ben and Olah, Chris and Olsson, Catherine and Amodei, Dario and Joseph, Nicholas and Kaplan, Jared and McCandlish, Sam , keywords =. Scaling Law...

    work page doi:10.48550/arxiv.2205.10487 2022

  69. [95]

    Scholarbert: Bigger is not always better, 2022

    Hong, Zhi and Ajith, Aswathy and Pauloski, Gregory and Duede, Eamon and Malamud, Carl and Magoulas, Roger and Chard, Kyle and Foster, Ian , keywords =. ScholarBERT: Bigger is Not Always Better , publisher =. 2022 , copyright =. doi:10.48550/ARXIV.2205.11342 , url =

    work page doi:10.48550/arxiv.2205.11342 2022

  70. [96]

    Advances in Neural Information Processing Systems , volume=

    Frank-Wolfe Bayesian quadrature: Probabilistic integration with theoretical guarantees , author=. Advances in Neural Information Processing Systems , volume=

    work page

  71. [97]

    Zhao, Ni Lao, Hongrae Lee, Da-Cheng Juan, and Kelvin Guu

    Gao, Luyu and Dai, Zhuyun and Pasupat, Panupong and Chen, Anthony and Chaganty, Arun Tejasvi and Fan, Yicheng and Zhao, Vincent Y. and Lao, Ni and Lee, Hongrae and Juan, Da-Cheng and Guu, Kelvin , keywords =. Attributed Text Generation via Post-hoc Research and Revision , publisher =. 2022 , copyright =. doi:10.48550/ARXIV.2210.08726 , url =

    work page doi:10.48550/arxiv.2210.08726 2022

  72. [98]

    Srivastava, Aarohi and Rastogi, Abhinav and Rao, Abhishek and Shoeb, Abu Awal Md and Abid, Abubakar and Fisch, Adam and Brown, Adam R. and Santoro, Adam and Gupta, Aditya and Garriga-Alonso, Adrià and Kluska, Agnieszka and Lewkowycz, Aitor and Agarwal, Akshat and Power, Alethea and Ray, Alex and Warstadt, Alex and Kocurek, Alexander W. and Safaya, Ali and...

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2206.04615 2022

  73. [99]

    Gpt-neox-20b: An open-source autoregressive language model

    Black, Sid and Biderman, Stella and Hallahan, Eric and Anthony, Quentin and Gao, Leo and Golding, Laurence and He, Horace and Leahy, Connor and McDonell, Kyle and Phang, Jason and Pieler, Michael and Prashanth, USVSN Sai and Purohit, Shivanshu and Reynolds, Laria and Tow, Jonathan and Wang, Ben and Weinbach, Samuel , keywords =. GPT-NeoX-20B: An Open-Sour...

    work page doi:10.48550/arxiv.2204.06745 2022

  74. [102]

    ArXiv , year=

    RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models , author=. ArXiv , year=

    work page

  75. [103]

    Bowman and Rachel Rudinger , title =

    Chandler May and Alex Wang and Shikha Bordia and Samuel R. Bowman and Rachel Rudinger , title =. CoRR , volume =. 2019 , url =. 1903.10561 , timestamp =

    work page arXiv 2019

  76. [104]

    Survey of hallucination in natural language generation,

    Ziwei Ji and Nayeon Lee and Rita Frieske and Tiezheng Yu and Dan Su and Yan Xu and Etsuko Ishii and Yejin Bang and Andrea Madotto and Pascale Fung , title =. CoRR , volume =. 2022 , url =. 2202.03629 , timestamp =

    work page arXiv 2022

  77. [106]

    Flamingo: a Visual Language Model for Few-Shot Learning

    Alayrac, Jean-Baptiste and Donahue, Jeff and Luc, Pauline and Miech, Antoine and Barr, Iain and Hasson, Yana and Lenc, Karel and Mensch, Arthur and Millican, Katie and Reynolds, Malcolm and Ring, Roman and Rutherford, Eliza and Cabi, Serkan and Han, Tengda and Gong, Zhitao and Samangooei, Sina and Monteiro, Marianne and Menick, Jacob and Borgeaud, Sebasti...

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2204.14198 2022

  78. [107]

    Wizard of wikipedia: Knowledge-powered conversational agents, 2018

    Dinan, Emily and Roller, Stephen and Shuster, Kurt and Fan, Angela and Auli, Michael and Weston, Jason , keywords =. Wizard of Wikipedia: Knowledge-Powered Conversational agents , publisher =. 2018 , copyright =. doi:10.48550/ARXIV.1811.01241 , url =

    work page doi:10.48550/arxiv.1811.01241 2018

  79. [110]

    AAAI , year=

    SciTaiL: A Textual Entailment Dataset from Science Question Answering , author=. AAAI , year=

    work page

  80. [112]

    EMNLP , year=

    Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning , author=. EMNLP , year=

    work page

Showing first 80 references.