A Unified Framework for Token-Level Watermarks

LLM watermarks allow tracing AI-generated texts by inserting a detectable signal into their generated content. Recent works have proposed a wide range of token-level watermarking algorithms, each with distinct designs. Crucially, there is currently no general and principled formulation for token-level LLM watermarking that could enable deeper understanding of current schemes and ease the design of new variants. In this work, we propose the first unified framework for token-level watermarks based on a principled constrained optimization problem. Our formulation unifies most existing and popular token-level watermarking methods, and explicitly reveals the constraints that each method optimizes. In the process, it highlights an underexplored quality-diversity-power trade-off. Our experimental evaluation validates our framework: watermarking schemes derived from a given constraint consistently maximize detection power with respect to that constraint. At the same time, the framework provides a principled approach for designing novel watermarking schemes tailored to specific requirements, for instance minimizing perplexity of the watermarked text.