{"slug": "a-watermark-for-large-language-models", "title": "A Watermark for Large Language Models", "summary": "Researchers at the University of Maryland and Facebook AI Research proposed a watermarking framework for proprietary large language models that embeds invisible signals into generated text, detectable by an efficient open-source algorithm without API or parameter access, with negligible impact on text quality. The method, tested on a multi-billion parameter model from the Open Pretrained Transformer (OPT) family, uses a randomized set of 'green' tokens promoted during sampling and a statistical test with interpretable p-values, as detailed in the paper 'A Watermark for Large Language Models' submitted to arXiv on January 24, 2023.", "body_md": "# Computer Science > Machine Learning\n\n[Submitted on 24 Jan 2023 (\n\n[v1](https://arxiv.org/abs/2301.10226v1)), last revised 1 May 2024 (this version, v4)]# Title:A Watermark for Large Language Models\n\n[View PDF](/pdf/2301.10226)\n\n[HTML (experimental)](https://arxiv.org/html/2301.10226v4)\n\nAbstract: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 \"green\" 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.\n\n## Submission history\n\nFrom: John Kirchenbauer [[view email](/show-email/48291975/2301.10226)]\n\n**Tue, 24 Jan 2023 18:52:59 UTC (3,550 KB)**\n\n[[v1]](/abs/2301.10226v1)**Fri, 27 Jan 2023 18:54:34 UTC (3,620 KB)**\n\n[[v2]](/abs/2301.10226v2)**Tue, 6 Jun 2023 17:50:01 UTC (3,618 KB)**\n\n[[v3]](/abs/2301.10226v3)**[v4]** Wed, 1 May 2024 22:04:31 UTC (3,825 KB)\n\n### Current browse context:\n\ncs.LG\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer\n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers\n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps\n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations\n\n*(*[What are Smart Citations?](https://www.scite.ai/))# Code, Data and Media Associated with this Article\n\nalphaXiv\n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers\n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub\n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub\n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face\n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast\n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower\n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender\n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))\nIArxiv Recommender\n\n*(*[What is IArxiv?](https://iarxiv.org/about))# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [ Learn more about arXivLabs](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/a-watermark-for-large-language-models", "canonical_source": "https://arxiv.org/abs/2301.10226", "published_at": "2026-08-10 22:48:28+00:00", "updated_at": "2026-08-10 23:11:10.934334+00:00", "lang": "en", "topics": ["large-language-models", "ai-safety", "ai-research"], "entities": ["University of Maryland", "Facebook AI Research", "Open Pretrained Transformer (OPT)", "arXiv"], "alternates": {"html": "https://wpnews.pro/news/a-watermark-for-large-language-models", "markdown": "https://wpnews.pro/news/a-watermark-for-large-language-models.md", "text": "https://wpnews.pro/news/a-watermark-for-large-language-models.txt", "jsonld": "https://wpnews.pro/news/a-watermark-for-large-language-models.jsonld"}}