Potential harms of Large Language Models such as mass misinformation and plagiarism can be partially mitigated if there exists a reliable way to detect machine generated text. In this paper, we propose a new watermarking method to detect machine-generated texts. Our method embeds a unique pattern within the generated text, ensuring that while the content remains coherent and natural to human readers, it carries distinct markers that can be identified algorithmically. Specifically, we intervene with the token sampling process in a way which enables us to trace back our token choices during the detection phase. We show how watermarking affects textual quality and compare our proposed method with a state-of-the-art watermarking method in terms of robustness and detectability. Through extensive experiments, we demonstrate the effectiveness of our watermarking scheme in distinguishing between watermarked and non-watermarked text, achieving high detection rates while maintaining textual quality.

翻译：大型语言模型（如大规模虚假信息和抄袭）的潜在危害，如果存在一种可靠的方法来检测机器生成的文本，可以在一定程度上得到缓解。在本文中，我们提出了一种新的水印方法用于检测机器生成的文本。我们的方法在生成的文本中嵌入一种独特的模式，确保在内容对人类读者保持连贯和自然的同时，携带可通过算法识别的独特标记。具体来说，我们对令牌采样过程进行干预，使得在检测阶段能够回溯我们的令牌选择。我们展示了水印如何影响文本质量，并将我们提出的方法与一种先进的水印方法在鲁棒性和可检测性方面进行了比较。通过大量实验，我们证明了我们水印方案在区分有水印和无水印文本方面的有效性，即在保持文本质量的同时实现了高检测率。