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Research questionHow can LLM watermarks reduce vocabulary-membership overhead without weakening statistical detection guarantees?LLM watermarks may require costly per-token vocabulary-membership and self-salting computations during generation. Reducing this overhead must preserve the statistical basis for reliably detecting watermarked text.
AI
Alignment & Safety
Inference Optimization
Natural Language Processing
Statistical Machine Learning
Latest papersRecent research connected to this question, newest first.Flip, Don't Shuffle: Watermarking LLMs at the Speed of InferenceThe source studies Stateless Bernoulli Watermarking for LLMs, using independent per-token Bernoulli trials and counter-based random numbers. Evidence covers null z-score behavior, end-to-end inference overhead, full-vocabulary self-salting, distributed-inference compatibility, two seeding schemes, eight parameter configurations, and comparisons with KGW and SynthID.research paper · Sep 3, 2026
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