@inproceedings{chen-etal-2025-improved,
    title = "Improved Unbiased Watermark for Large Language Models",
    author = "Chen, Ruibo  and
      Wu, Yihan  and
      Guo, Junfeng  and
      Huang, Heng",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1005/",
    pages = "20587--20601",
    ISBN = "979-8-89176-251-0",
    abstract = "As artificial intelligence surpasses human capabilities in text generation, the necessity to authenticate the origins of AI-generated content has become paramount. Unbiased watermarks offer a powerful solution by embedding statistical signals into language model-generated text without distorting the quality. In this paper, we introduce MCmark, a family of unbiased, Multi-Channel-based watermarks. MCmark works by partitioning the model{'}s vocabulary into segments and promoting token probabilities within a selected segment based on a watermark key. We demonstrate that MCmark not only preserves the original distribution of the language model but also offers significant improvements in detectability and robustness over existing unbiased watermarks. Our experiments with widely-used language models demonstrate an improvement in detectability of over 10{\%} using MCmark, compared to existing state-of-the-art unbiased watermarks. This advancement underscores MCmark{'}s potential in enhancing the practical application of watermarking in AI-generated texts."
}