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Copyright Traps for Large Language Models
Feb. 15, 2024, 5:46 a.m. | Matthieu Meeus, Igor Shilov, Manuel Faysse, Yves-Alexandre de Montjoye
cs.CL updates on arXiv.org arxiv.org
Abstract: Questions of fair use of copyright-protected content to train Large Language Models (LLMs) are being very actively debated. Document-level inference has been proposed as a new task: inferring from black-box access to the trained model whether a piece of content has been seen during training. SOTA methods however rely on naturally occurring memorization of (part of) the content. While very effective against models that memorize a lot, we hypothesize--and later confirm--that they will not work …
abstract arxiv box copyright cs.cl cs.cr document fair fair use inference language language models large language large language models llms questions sota train training type
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