Work

From Trade-off to Synergy: A Versatile Symbiotic Watermarking Framework for Large Language Models

Year: 2025

Type: conference-paper

Abstract: The rise of Large Language Models (LLMs) has heightened concerns about the misuse of AI-generated text, making watermarking a promising solution.Mainstream watermarking schemes for LLMs fall into two categories: logits-based and sampling-based.However, current schemes entail trade-offs among robustness, text quality, and security.To mitigate this, we integrate logits-based and sampling-based schemes, harnessing their respective strengths to achieve synergy.In this paper, we propose a versatile symbiotic watermarking framework with three strategies: serial, parallel, and hybrid.The hybrid framework adaptively embeds watermarks using token entropy and semantic entropy, optimizing the balance between detectability, robustness, text quality, and security.Furthermore, we validate our approach through comprehensive experiments on various datasets and models.Experimental results indicate that our method outperforms existing baselines and achieves state-of-the-art (SOTA) performance.We believe this framework provides novel insights into diverse watermarking paradigms.Our code is available at https://github.com/redwyd/SymMark.

Source: Annual Meeting of the Association for Computational Linguistics (ACL)

DOI: 10.18653/v1/2025.acl-long.509

Authors: Yidan Wang, Yubing Ren, Yanan Cao, Binxing Fang

Institutions: Chinese Academy of Sciences, Institute of Information Engineering, University of Chinese Academy of Sciences

Language: English

Topic: Advanced Steganography and Watermarking Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: National Natural Science Foundation of China

Award: G5077752208