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M2D2: A Massively Multi-Domain Language Modeling Dataset., , , and . EMNLP, page 964-975. Association for Computational Linguistics, (2022)DataComp-LM: In search of the next generation of training sets for language models., , , , , , , , , and 49 other author(s). CoRR, (2024)LESS: Selecting Influential Data for Targeted Instruction Tuning., , , , and . ICML, OpenReview.net, (2024)Language models scale reliably with over-training and on downstream tasks., , , , , , , , , and 13 other author(s). CoRR, (2024)Show Your Work: Improved Reporting of Experimental Results., , , , and . EMNLP/IJCNLP (1), page 2185-2194. Association for Computational Linguistics, (2019)Time is Encoded in the Weights of Finetuned Language Models., , and . 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., , , , , , and . ACL (1), page 7393-7420. Association for Computational Linguistics, (2024)Don't Stop Pretraining: Adapt Language Models to Domains and Tasks, , , , , , and . (2020)cite arxiv:2004.10964Comment: ACL 2020.kNN-Prompt: Nearest Neighbor Zero-Shot Inference, , , and . (2022)cite arxiv:2205.13792.Detoxifying Language Models Risks Marginalizing Minority Voices., , , , , and . NAACL-HLT, page 2390-2397. Association for Computational Linguistics, (2021)