[Submitted on 24 Jan 2023 (
[v1](https://arxiv.org/abs/2301.10226v1)), last revised 1 May 2024 (this version, v4)]# Title:A Watermark for Large Language Models
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Abstract: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.
Submission history #
From: John Kirchenbauer [[view email](/show-email/48291975/2301.10226)]
**Tue, 24 Jan 2023 18:52:59 UTC (3,550 KB)**
[[v1]](/abs/2301.10226v1)**Fri, 27 Jan 2023 18:54:34 UTC (3,620 KB)**
[[v2]](/abs/2301.10226v2)**Tue, 6 Jun 2023 17:50:01 UTC (3,618 KB)**
[[v3]](/abs/2301.10226v3)**[v4]** Wed, 1 May 2024 22:04:31 UTC (3,825 KB)
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