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Don't Stop Pretraining: Adapt Language Models to Domains and Tasks

S. Gururangan, A. Marasović, S. Swayamdipta, K. Lo, I. Beltagy, D. Downey, and N. Smith. (2020)cite arxiv:2004.10964Comment: ACL 2020.

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M2D2: A Massively Multi-Domain Language Modeling Dataset.M. Reid, V. Zhong, S. Gururangan, and L. Zettlemoyer. EMNLP, page 964-975. Association for Computational Linguistics, (2022)DataComp-LM: In search of the next generation of training sets for language models.J. Li, A. Fang, G. Smyrnis, M. Ivgi, M. Jordan, S. Gadre, H. Bansal, E. Guha, S. Keh, K. Arora and 49 other author(s). CoRR, (2024)LESS: Selecting Influential Data for Targeted Instruction Tuning.M. Xia, S. Malladi, S. Gururangan, S. Arora, and D. Chen. ICML, OpenReview.net, (2024)Language models scale reliably with over-training and on downstream tasks.S. Gadre, G. Smyrnis, V. Shankar, S. Gururangan, M. Wortsman, R. Shao, J. Mercat, A. Fang, J. Li, S. Keh and 13 other author(s). CoRR, (2024)Show Your Work: Improved Reporting of Experimental Results.J. Dodge, S. Gururangan, D. Card, R. Schwartz, and N. Smith. EMNLP/IJCNLP (1), page 2185-2194. Association for Computational Linguistics, (2019)Time is Encoded in the Weights of Finetuned Language Models.K. Nylund, S. Gururangan, and N. Smith. ACL (1), page 2571-2587. Association for Computational Linguistics, (2024)AboutMe: Using Self-Descriptions in Webpages to Document the Effects of English Pretraining Data Filters.L. Lucy, S. Gururangan, L. Soldaini, E. Strubell, D. Bamman, L. Klein, and J. Dodge. ACL (1), page 7393-7420. Association for Computational Linguistics, (2024)Detoxifying Language Models Risks Marginalizing Minority Voices.A. Xu, E. Pathak, E. Wallace, S. Gururangan, M. Sap, and D. Klein. NAACL-HLT, page 2390-2397. Association for Computational Linguistics, (2021)Expected Validation Performance and Estimation of a Random Variable's Maximum.J. Dodge, S. Gururangan, D. Card, R. Schwartz, and N. Smith. EMNLP (Findings), page 4066-4073. Association for Computational Linguistics, (2021)Whose Language Counts as High Quality? Measuring Language Ideologies in Text Data Selection.S. Gururangan, D. Card, S. Dreier, E. Gade, L. Wang, Z. Wang, L. Zettlemoyer, and N. Smith. EMNLP, page 2562-2580. Association for Computational Linguistics, (2022)

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Don't Stop Pretraining: Adapt Language Models to Domains and Tasks

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Don't Stop Pretraining: Adapt Language Models to Domains and Tasks

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Don't Stop Pretraining: Adapt Language Models to Domains and Tasks