Watermarking for Large Language Models: A Survey

Zhiguang Yang, Gejian Zhao and Hanzhou Wu ()
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Zhiguang Yang: School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
Gejian Zhao: School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
Hanzhou Wu: School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China

Mathematics, 2025, vol. 13, issue 9, 1-27

Abstract: With the rapid advancement and widespread deployment of large language models (LLMs), concerns regarding content provenance, intellectual property protection, and security threats have become increasingly prominent. Watermarking techniques have emerged as a promising solution for embedding verifiable signals into model outputs, enabling attribution, authentication, and mitigation of unauthorized usage. Despite growing interest in watermarking LLMs, the field lacks a systematic review to consolidate existing research and assess the effectiveness of different techniques. Key challenges include the absence of a unified taxonomy and limited understanding of trade-offs between capacity, robustness, and imperceptibility in real-world scenarios. This paper addresses these gaps by providing a comprehensive survey of watermarking methods tailored to LLMs, structured around three core contributions: (1) We classify these methods as training-free and training-based approaches and detail their mechanisms, strengths, and limitations to establish a structured understanding of existing techniques. (2) We evaluate these techniques based on key criteria—including robustness, imperceptibility, and payload capacity—to identify their effectiveness and limitations, highlighting challenges in designing resilient and practical watermarking solutions. (3) We also discuss critical open challenges while outlining future research directions and practical considerations to drive innovation in watermarking for LLMs. By providing a structured synthesis, this work advances the development of secure and effective watermarking solutions for LLMs.

Keywords: watermarking; large language models; security; deep learning; information hiding (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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