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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.19378 |
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| _version_ | 1866918259172835328 |
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| author | Hu, Zhiqing Zhao, Chenxu Lu, Jiazhong Liu, Xiaolei |
| author_facet | Hu, Zhiqing Zhao, Chenxu Lu, Jiazhong Liu, Xiaolei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_19378 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HATS: High-Accuracy Triple-Set Watermarking for Large Language Models Hu, Zhiqing Zhao, Chenxu Lu, Jiazhong Liu, Xiaolei Computation and Language 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. |
| title | HATS: High-Accuracy Triple-Set Watermarking for Large Language Models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2512.19378 |