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Language models scale reliably with over-training and on downstream tasks., , , , , , , , , and 13 other author(s). CoRR, (2024)Time is Encoded in the Weights of Finetuned Language Models., , and . CoRR, (2023)AboutMe: Using Self-Descriptions in Webpages to Document the Effects of English Pretraining Data Filters., , , , , , and . CoRR, (2024)Show Your Work: Improved Reporting of Experimental Results., , , , and . EMNLP/IJCNLP (1), page 2185-2194. Association for Computational Linguistics, (2019)Scaling Expert Language Models with Unsupervised Domain Discovery., , , , , , and . CoRR, (2023)lo-fi: distributed fine-tuning without communication., , , , , , and . CoRR, (2022)LESS: Selecting Influential Data for Targeted Instruction Tuning., , , , and . CoRR, (2024)M2D2: A Massively Multi-Domain Language Modeling Dataset., , , and . EMNLP, page 964-975. Association for Computational Linguistics, (2022)Nearest Neighbor Zero-Shot Inference., , , and . EMNLP, page 3254-3265. Association for Computational Linguistics, (2022)Variational Pretraining for Semi-supervised Text Classification., , , and . ACL (1), page 5880-5894. Association for Computational Linguistics, (2019)