



01439nas a2200169   4500008004100000245004200041210004000083260001300123520098600136100002301122700001901145700001501164700001901179700001501198700001901213856003701232       2023                        eng d00aA Watermark for Large Language Models0 aWatermark for Large Language Models  c6/6/20233 a<p>Potential harms of large language models can be mitigated by watermarking model output, i.e., embedding signals into generated text that are invisible to humans but algorithmically detectable from a short span of tokens. We propose a watermarking framework for proprietary language models. The watermark can be embedded with negligible impact on text quality, and can be detected using an efficient open-source algorithm without access to the language model API or parameters. The watermark works by selecting a randomized set of &quot;green&quot; tokens before a word is generated, and then softly promoting use of green tokens during sampling. We propose a statistical test for detecting the watermark with interpretable p-values, and derive an information-theoretic framework for analyzing the sensitivity of the watermark. We test the watermark using a multi-billion parameter model from the Open Pretrained Transformer (OPT) family, and discuss robustness and security.</p>1 aKirchenbauer, John1 aGeiping, Jonas1 aWen, Yuxin1 aKatz, Jonathan1 aMiers, Ian1 aGoldstein, Tom  uhttps://arxiv.org/abs/2301.10226