HATS: High-Accuracy Triple-Set Watermarking for Large Language Models

Zhiqing Hu; Chenxu Zhao; Jiazhong Lu; Xiaolei Liu

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arXiv · 2512.19378

HATS: High-Accuracy Triple-Set Watermarking for Large Language Models

Zhiqing Hu·Chenxu Zhao·Jiazhong Lu·Xiaolei Liu

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Abstract

Misuse of LLM-generated text can be curbed by watermarking techniques that embed implicit signals into the output. We propose a watermark that partitions the vocabulary at each decoding step into three sets (Green/Yellow/Red) with fixed ratios and restricts sampling to the Green and Yellow sets. At detection time, we replay the same partitions, compute Green-enrichment and Red-depletion statistics, convert them to one-sided z-scores, and aggregate their p-values via Fisher's method to decide whether a passage is watermarked. We implement generation, detection, and testing on Llama 2 7B, and evaluate true-positive rate, false-positive rate, and text quality. Results show that the triple-partition scheme achieves high detection accuracy at fixed FPR while preserving readability.

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Zhiqing Hu, Chenxu Zhao, Jiazhong Lu, Xiaolei Liu. 2025-12-22. HATS: High-Accuracy Triple-Set Watermarking for Large Language Models. https://arxiv.org/abs/2512.19378

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