A Watermark for Black-Box Language Models
<|reference_start|>A Watermark for Black-Box Language Models: Watermarking has recently emerged as an effective strategy for detecting the outputs of large language models (LLMs). Most existing schemes require \emph{white-box} access to the model's next-token probability distribution, which is typically not accessible to downstream users of an LLM API. In this work, we propose a principled watermarking scheme that requires only the ability to sample sequences from the LLM (i.e. \emph{black-box} access), boasts a \emph{distortion-free} property, and can be chained or nested using multiple secret keys. We provide performance guarantees, demonstrate how it can be leveraged when white-box access is available, and show when it can outperform existing white-box schemes via comprehensive experiments.<|reference_end|>
@article{bahri2024a,
    title={A Watermark for Black-Box Language Models},
    author={Dara Bahri, John Wieting, Dana Alon, Donald Metzler},
    journal={arXiv preprint arXiv:2410.02099},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.02099},
    primaryClass={cs.CR cs.CL cs.LG}
}