Anchored Decoding: Provably Reducing Copyright Risk for Any Language Model 事件

Anchored Decoding: Provably Reducing Copyright Risk for Any Language Model arXiv:2602.07120v2 Announce Type: replace Abstract: Language models (LMs) tend to memorize portions of their training data and emit verbatim spans. When the underlying sources are sensitive or copyright-protected, such reproduction raises issues of consent and compensation for creators and compliance risks for developers. We propose Anchored Decoding, a plug-and-play inference-time method for suppressing verbatim copying