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Optimizing watermarks for large language models
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With the rise of large language models (LLMs) and concerns about potential misuse, watermarks for generative LLMs have recently attracted much attention. An important aspect of such watermarks is the trade-off between their identifiability and their impact on the quality of the generated text. This paper introduces a systematic approach to this trade-off in terms of a multi-objective optimization problem. For a large class of robust, efficient watermarks, the associated Pareto optimal solutions are identified and shown to outperform the currently default watermark.
Forward citations
Cited by 2 Pith papers
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SAMark: A Self-Anchored Text Watermarking with Paragraph-Level Paraphrase Robustness
SAMark uses self-anchored semantic green regions, multi-channel hyperbolic scoring, and diversity-aware filtering to reach 90.2% TP@FP1% detection under paragraph paraphrasing while preserving text quality.
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Autoregressive Images Watermarking through Lexical Biasing: An Approach Resistant to Regeneration Attack
LBW embeds watermarks into autoregressive image token maps by biasing token sampling toward a secret green list and detects them with a z-test on green-token counts.
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