Towards Better Statistical Understanding of Watermarking LLMs
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914449309302784 |
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| author | Cai, Zhongze Liu, Shang Wang, Hanzhao Zhong, Huaiyang Li, Xiaocheng |
| author_facet | Cai, Zhongze Liu, Shang Wang, Hanzhao Zhong, Huaiyang Li, Xiaocheng |
| contents | In this paper, we study the problem of watermarking large language models (LLMs). We consider the trade-off between model distortion and detection ability and formulate it as a constrained optimization problem based on the red-green list watermarking algorithm. We show that the optimal solution to the optimization problem enjoys a nice analytical property which provides a better understanding and inspires the algorithm design for the watermarking process. We develop an online dual gradient ascent watermarking algorithm in light of this optimization formulation and prove its asymptotic Pareto optimality between model distortion and detection ability. Such a result guarantees an averaged increased green list probability and henceforth detection ability explicitly (in contrast to previous results). Moreover, we provide a systematic discussion on the choice of the model distortion metrics for the watermarking problem. We justify our choice of KL divergence and present issues with the existing criteria of ``distortion-free'' and perplexity. Finally, we empirically evaluate our algorithms on extensive datasets against benchmark algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_13027 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Better Statistical Understanding of Watermarking LLMs Cai, Zhongze Liu, Shang Wang, Hanzhao Zhong, Huaiyang Li, Xiaocheng Machine Learning Cryptography and Security Information Theory In this paper, we study the problem of watermarking large language models (LLMs). We consider the trade-off between model distortion and detection ability and formulate it as a constrained optimization problem based on the red-green list watermarking algorithm. We show that the optimal solution to the optimization problem enjoys a nice analytical property which provides a better understanding and inspires the algorithm design for the watermarking process. We develop an online dual gradient ascent watermarking algorithm in light of this optimization formulation and prove its asymptotic Pareto optimality between model distortion and detection ability. Such a result guarantees an averaged increased green list probability and henceforth detection ability explicitly (in contrast to previous results). Moreover, we provide a systematic discussion on the choice of the model distortion metrics for the watermarking problem. We justify our choice of KL divergence and present issues with the existing criteria of ``distortion-free'' and perplexity. Finally, we empirically evaluate our algorithms on extensive datasets against benchmark algorithms. |
| title | Towards Better Statistical Understanding of Watermarking LLMs |
| topic | Machine Learning Cryptography and Security Information Theory |
| url | https://arxiv.org/abs/2403.13027 |