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Annotation Artifacts in Natural Language Inference Data.

S. Gururangan, S. Swayamdipta, O. Levy, R. Schwartz, S. Bowman, and N. Smith. NAACL-HLT (2), page 107-112. Association for Computational Linguistics, (2018)

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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)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)Don't Stop Pretraining: Adapt Language Models to Domains and TasksS. Gururangan, A. Marasović, S. Swayamdipta, K. Lo, I. Beltagy, D. Downey, and N. Smith. (2020)cite arxiv:2004.10964Comment: ACL 2020.kNN-Prompt: Nearest Neighbor Zero-Shot InferenceW. Shi, J. Michael, S. Gururangan, and L. Zettlemoyer. (2022)cite arxiv:2205.13792.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)Annotation Artifacts in Natural Language Inference Data.S. Gururangan, S. Swayamdipta, O. Levy, R. Schwartz, S. Bowman, and N. Smith. NAACL-HLT (2), page 107-112. Association for Computational Linguistics, (2018)

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Annotation Artifacts in Natural Language Inference Data.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Annotation Artifacts in Natural Language Inference Data.

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Annotation Artifacts in Natural Language Inference Data.