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Real or Fake Text?: Investigating Human Ability to Detect Boundaries between Human-Written and Machine-Generated Text.

, , , , and . AAAI, page 12763-12771. AAAI Press, (2023)

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A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity., , , , , , , , , and 1 other author(s). CoRR, (2023)Report of the 1st Workshop on Generative AI and Law., , , , , , , , , and 25 other author(s). CoRR, (2023)Quantifying Memorization Across Neural Language Models., , , , , and . ICLR, OpenReview.net, (2023)Automatic Detection of Generated Text is Easiest when Humans are Fooled., , , and . ACL, page 1808-1822. Association for Computational Linguistics, (2020)Contact Tracing Mobile Apps for COVID-19: Privacy Considerations and Related Trade-offs., , and . CoRR, (2020)Inherent privacy limitations of decentralized contact tracing apps., , , , , , , and . J. Am. Medical Informatics Assoc., 28 (1): 193-195 (2021)ChatEval: A Tool for Chatbot Evaluation., , , , , and . NAACL-HLT (Demonstrations), page 60-65. Association for Computational Linguistics, (2019)Extracting Training Data from Diffusion Models., , , , , , , , and . USENIX Security Symposium, page 5253-5270. USENIX Association, (2023)Real or Fake Text?: Investigating Human Ability to Detect Boundaries between Human-Written and Machine-Generated Text., , , , and . AAAI, page 12763-12771. AAAI Press, (2023)SynthBio: A Case Study in Faster Curation of Text Datasets., , , , , and . NeurIPS Datasets and Benchmarks, (2021)