  We study the problem of watermarking large language models (LLMs) generated
text -- one of the most promising approaches for addressing the safety
challenges of LLM usage. In this paper, we propose a rigorous theoretical
framework to quantify the effectiveness and robustness of LLM watermarks. We
propose a robust and high-quality watermark method, Unigram-Watermark, by
extending an existing approach with a simplified fixed grouping strategy. We
prove that our watermark method enjoys guaranteed generation quality,
correctness in watermark detection, and is robust against text editing and
paraphrasing. Experiments on three varying LLMs and two datasets verify that
our Unigram-Watermark achieves superior detection accuracy and comparable
generation quality in perplexity, thus promoting the responsible use of LLMs.
Code is available at https://github.com/XuandongZhao/Unigram-Watermark.
