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        <title>Efficiently Identifying Watermarked Segments in Mixed-Source Texts</title>
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    <abstract>Text watermarks in large language models (LLMs) are increasingly used to detect synthetic text, mitigating misuse cases like fake news and academic dishonesty. While existing watermarking detection techniques primarily focus on classifying entire documents as watermarked or not, they often neglect the common scenario of identifying individual watermark segments within longer, mixed-source documents. Drawing inspiration from plagiarism detection systems, we propose two novel methods for partial watermark detection. First, we develop a geometry cover detection framework aimed at determining whether there is a watermark segment in long text. Second, we introduce an adaptive online learning algorithm to pinpoint the precise location of watermark segments within the text. Evaluated on three popular watermarking techniques (KGW-Watermark, Unigram-Watermark, and Gumbel-Watermark), our approach achieves high accuracy, significantly outperforming baseline methods. Moreover, our framework is adaptable to other watermarking techniques, offering new insights for precise watermark detection. Our code is publicly available at https://github.com/XuandongZhao/llm-watermark-location.</abstract>
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%0 Conference Proceedings
%T Efficiently Identifying Watermarked Segments in Mixed-Source Texts
%A Zhao, Xuandong
%A Liao, Chenwen
%A Wang, Yu-Xiang
%A Li, Lei
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F zhao-etal-2025-efficiently
%X Text watermarks in large language models (LLMs) are increasingly used to detect synthetic text, mitigating misuse cases like fake news and academic dishonesty. While existing watermarking detection techniques primarily focus on classifying entire documents as watermarked or not, they often neglect the common scenario of identifying individual watermark segments within longer, mixed-source documents. Drawing inspiration from plagiarism detection systems, we propose two novel methods for partial watermark detection. First, we develop a geometry cover detection framework aimed at determining whether there is a watermark segment in long text. Second, we introduce an adaptive online learning algorithm to pinpoint the precise location of watermark segments within the text. Evaluated on three popular watermarking techniques (KGW-Watermark, Unigram-Watermark, and Gumbel-Watermark), our approach achieves high accuracy, significantly outperforming baseline methods. Moreover, our framework is adaptable to other watermarking techniques, offering new insights for precise watermark detection. Our code is publicly available at https://github.com/XuandongZhao/llm-watermark-location.
%R 10.18653/v1/2025.acl-long.316
%U https://aclanthology.org/2025.acl-long.316/
%U https://doi.org/10.18653/v1/2025.acl-long.316
%P 6304-6316

Markdown (Informal)

[Efficiently Identifying Watermarked Segments in Mixed-Source Texts](https://aclanthology.org/2025.acl-long.316/) (Zhao et al., ACL 2025)

• Efficiently Identifying Watermarked Segments in Mixed-Source Texts (Zhao et al., ACL 2025)

ACL

• Xuandong Zhao, Chenwen Liao, Yu-Xiang Wang, and Lei Li. 2025. Efficiently Identifying Watermarked Segments in Mixed-Source Texts. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6304–6316, Vienna, Austria. Association for Computational Linguistics.