https://arxiv.org/abs/2505.12546 close this message arXiv smileybones arXiv Is Hiring a DevOps Engineer Work on one of the world's most important websites and make an impact on open science. View Jobs Skip to main content Cornell University arXiv Is Hiring a DevOps Engineer View Jobs We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate arxiv logo > cs > arXiv:2505.12546 [ ] Help | Advanced Search [All fields ] Search arXiv logo Cornell University Logo [ ] GO quick links * Login * Help Pages * About Computer Science > Computation and Language arXiv:2505.12546 (cs) [Submitted on 18 May 2025] Title:Extracting memorized pieces of (copyrighted) books from open-weight language models Authors:A. Feder Cooper, Aaron Gokaslan, Amy B. Cyphert, Christopher De Sa, Mark A. Lemley, Daniel E. Ho, Percy Liang View a PDF of the paper titled Extracting memorized pieces of (copyrighted) books from open-weight language models, by A. Feder Cooper and Aaron Gokaslan and Amy B. Cyphert and Christopher De Sa and Mark A. Lemley and Daniel E. Ho and Percy Liang View PDF Abstract:Plaintiffs and defendants in copyright lawsuits over generative AI often make sweeping, opposing claims about the extent to which large language models (LLMs) have memorized plaintiffs' protected expression. Drawing on adversarial ML and copyright law, we show that these polarized positions dramatically oversimplify the relationship between memorization and copyright. To do so, we leverage a recent probabilistic extraction technique to extract pieces of the Books3 dataset from 13 open-weight LLMs. Through numerous experiments, we show that it's possible to extract substantial parts of at least some books from different LLMs. This is evidence that the LLMs have memorized the extracted text; this memorized content is copied inside the model parameters. But the results are complicated: the extent of memorization varies both by model and by book. With our specific experiments, we find that the largest LLMs don't memorize most books -- either in whole or in part. However, we also find that Llama 3.1 70B memorizes some books, like Harry Potter and 1984, almost entirely. We discuss why our results have significant implications for copyright cases, though not ones that unambiguously favor either side. Subjects: Computation and Language (cs.CL); Computers and Society (cs.CY); Machine Learning (cs.LG) Cite as: arXiv:2505.12546 [cs.CL] (or arXiv:2505.12546v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2505.12546 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: A. Feder Cooper [view email] [v1] Sun, 18 May 2025 21:06:32 UTC (23,852 KB) Full-text links: Access Paper: View a PDF of the paper titled Extracting memorized pieces of (copyrighted) books from open-weight language models, by A. Feder Cooper and Aaron Gokaslan and Amy B. Cyphert and Christopher De Sa and Mark A. 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