Entropy-Guided Watermarking for LLMs: A Test-Time Framework for Robust and Traceable Text Generation
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866912331440586752 |
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| author | Cai, Shizhan Ding, Liang Tao, Dacheng |
| author_facet | Cai, Shizhan Ding, Liang Tao, Dacheng |
| contents | The rapid development of Large Language Models (LLMs) has intensified concerns about content traceability and potential misuse. Existing watermarking schemes for sampled text often face trade-offs between maintaining text quality and ensuring robust detection against various attacks. To address these issues, we propose a novel watermarking scheme that improves both detectability and text quality by introducing a cumulative watermark entropy threshold. Our approach is compatible with and generalizes existing sampling functions, enhancing adaptability. Experimental results across multiple LLMs show that our scheme significantly outperforms existing methods, achieving over 80\% improvements on widely-used datasets, e.g., MATH and GSM8K, while maintaining high detection accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_12108 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Entropy-Guided Watermarking for LLMs: A Test-Time Framework for Robust and Traceable Text Generation Cai, Shizhan Ding, Liang Tao, Dacheng Computation and Language The rapid development of Large Language Models (LLMs) has intensified concerns about content traceability and potential misuse. Existing watermarking schemes for sampled text often face trade-offs between maintaining text quality and ensuring robust detection against various attacks. To address these issues, we propose a novel watermarking scheme that improves both detectability and text quality by introducing a cumulative watermark entropy threshold. Our approach is compatible with and generalizes existing sampling functions, enhancing adaptability. Experimental results across multiple LLMs show that our scheme significantly outperforms existing methods, achieving over 80\% improvements on widely-used datasets, e.g., MATH and GSM8K, while maintaining high detection accuracy. |
| title | Entropy-Guided Watermarking for LLMs: A Test-Time Framework for Robust and Traceable Text Generation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2504.12108 |