Anchored Decoding: Provably Reducing Copyright Risk for Any Language Model
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: it enables decoding from any risky LM trained on mixed-license data by keeping generation in bounded proximity to a permissively trained safe LM. Anchored Decoding adaptively allocates a user-chosen information budget over the generation trajectory and enforces per-step constraints that yield a sequence-level guarantee, e...
| Authors | Jacqueline He, Jonathan Hayase, Wen-tau Yih et al. |
|---|---|
| Primary category | cs.CL |
| Published | 6 February 2026 |
| Last updated at source | 31 August 2026 |
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