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