Topic-Based Watermarks for LLM-Generated Text
<|reference_start|>Topic-Based Watermarks for LLM-Generated Text: The indistinguishability of text generated by large language models (LLMs) from human-generated text poses significant challenges. Watermarking algorithms are potential solutions by embedding detectable signatures within LLM-generated outputs. However, current watermarking schemes lack robustness to a range of attacks such as text substitution or manipulation, undermining their reliability. This paper proposes a novel topic-based watermarking algorithm for LLMs, designed to enhance the robustness of watermarking in LLMs. Our approach leverages the topics extracted from input prompts or outputs of non-watermarked LLMs in the generation process of watermarked text. We dynamically utilize token lists on identified topics and adjust token sampling weights accordingly. By using these topic-specific token biases, we embed a topic-sensitive watermarking into the generated text. We outline the theoretical framework of our topic-based watermarking algorithm and discuss its potential advantages in various scenarios. Additionally, we explore a comprehensive range of attacks against watermarking algorithms, including discrete alterations, paraphrasing, and tokenizations. We demonstrate that our proposed watermarking scheme classifies various watermarked text topics with 99.99% confidence and outperforms existing algorithms in terms of z-score robustness and the feasibility of modeling text degradation by potential attackers, while considering the trade-offs between the benefits and losses of watermarking LLM-generated text.<|reference_end|>
@article{nemecek2024topic-based,
    title={Topic-Based Watermarks for LLM-Generated Text},
    author={Alexander Nemecek, Yuzhou Jiang, and Erman Ayday},
    journal={arXiv preprint arXiv:2404.02138},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.02138},
    primaryClass={cs.CR cs.CL cs.LG}
}