No Free Lunch in LLM Watermarking: Trade-offs in Watermarking Design Choices
<|reference_start|>No Free Lunch in LLM Watermarking: Trade-offs in Watermarking Design Choices: Advances in generative models have made it possible for AI-generated text, code, and images to mirror human-generated content in many applications. Watermarking, a technique that aims to embed information in the output of a model to verify its source, is useful for mitigating the misuse of such AI-generated content. However, we show that common design choices in LLM watermarking schemes make the resulting systems surprisingly susceptible to attack -- leading to fundamental trade-offs in robustness, utility, and usability. To navigate these trade-offs, we rigorously study a set of simple yet effective attacks on common watermarking systems, and propose guidelines and defenses for LLM watermarking in practice.<|reference_end|>
@article{pang2024no,
    title={No Free Lunch in LLM Watermarking: Trade-offs in Watermarking Design
  Choices},
    author={Qi Pang, Shengyuan Hu, Wenting Zheng, Virginia Smith},
    journal={arXiv preprint arXiv:2402.16187},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.16187},
    primaryClass={cs.CR cs.CL cs.LG}
}