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A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity., , , , , , , , , и 1 other автор(ы). CoRR, (2023)Report of the 1st Workshop on Generative AI and Law., , , , , , , , , и 25 other автор(ы). CoRR, (2023)Comparison of Diverse Decoding Methods from Conditional Language Models., , , , и . ACL (1), стр. 3752-3762. Association for Computational Linguistics, (2019)Deduplicating Training Data Makes Language Models Better., , , , , , и . ACL (1), стр. 8424-8445. Association for Computational Linguistics, (2022)Reverse-Engineering Decoding Strategies Given Blackbox Access to a Language Generation System., , , , и . INLG, стр. 396-406. Association for Computational Linguistics, (2023)Preventing Generation of Verbatim Memorization in Language Models Gives a False Sense of Privacy., , , , , , , и . INLG, стр. 28-53. Association for Computational Linguistics, (2023)Measuring Forgetting of Memorized Training Examples., , , , , , , , , и 1 other автор(ы). ICLR, OpenReview.net, (2023)Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models., , , , , , , , , и 440 other автор(ы). CoRR, (2022)RoFT: A Tool for Evaluating Human Detection of Machine-Generated Text., , , и . EMNLP (Demos), стр. 189-196. Association for Computational Linguistics, (2020)Automatic Detection of Generated Text is Easiest when Humans are Fooled., , , и . ACL, стр. 1808-1822. Association for Computational Linguistics, (2020)