Title: WaterSearch: A Quality-Aware Search-based Watermarking Framework for Large Language Models
Primary area: foundation or frontier models, including LLMs
Keywords: LLM, text watermark, watermark detection

Abstract: In the era of large language models (LLMs), watermarking serves as a crucial safeguard for ensuring accountability, authenticity, and trust in machine-generated text. Text generated by LLMs can be identified through watermarking techniques, enabling the traceability and security of model outputs. Existing watermarking methods primarily embed signals by manipulating token generation probabilities, with detection achieved by computing corresponding statistical measures. Despite their effectiveness, these methods face a trade-off between detectability and text quality. In particular, watermarking strength and the randomness of watermark patterns may degrade the quality of generated text.

This paper proposes WaterSearch, a novel sentence-level search-based watermarking framework that overcomes this trade-off by jointly optimizing text quality (relative to unwatermarked distributions) and watermark detection confidence. Building on this framework, we further introduce a new watermark detection algorithm with theoretical guarantees. WaterSearch is highly versatile and can be integrated with various watermarking algorithms. We evaluate WaterSearch on three mainstream LLMs across ten diverse tasks. Extensive experiments show that our method achieves an average performance improvement of 51.01\% over popular baselines under the same watermark detectability strength. For challenging scenarios such as short texts and low-entropy generation, WaterSearch shows improvement of 47.78\% and 36.47\% respectively, demonstrating strong robustness. Moreover, under watermark attack settings, our method maintains high detectability even with 80\% word-level perturbations.