Token-Level Attribution of Multi-Mode LLM Watermarks: Optimal Rule and Belief Weighting
New statistical framework for attributing LLM-generated text to specific watermark keys while controlling Type I error — relevant for content-provenance verification.
Summary written by editorial AI · Source link below
arXiv:2610.23601v1 Announce Type: new Abstract: A watermark verifier may hold many modes, i.e., secret keys, and a document may mix spans of different modes with unwatermarked text. Directly reporting the mode with the largest pivotal statistic (pivot) inflates the Type I error, and restoring the nominal level reduces power as candidate modes are added. We formulate token-level multi-mode attribution for the Gumbel-max watermark as maximizing correct-label power under a Type I error constraint
Editorial Analysis
As EU AI Act transparency requirements advance, robust watermark attribution may become essential for verifying AI-generated content provenance.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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