Improved Unbiased Watermark for Large Language Models

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 modelgenerated 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 widelyused language models demonstrate an improvement in detectability of over 10% using MC-MARK, compared to existing state-of-the-art unbiased watermarks. This advancement underscores MCMARK's potential in enhancing the practical application of watermarking in AIgenerated texts. Our code is available at https: //github.com/RayRuiboChen/MCMark .