从权衡到协同：面向大语言模型的通用共生水印框架
From Trade-off to Synergy: A Versatile Symbiotic Watermarking Framework for Large Language Models
AI总结：
针对现有大语言模型水印方案在鲁棒性、文本质量与安全性间存在权衡的问题，提出融合两类水印方案的通用共生水印框架，经实验验证性能达SOTA，为水印范式研究提供新思路。
摘要：
大语言模型（LLMs）的兴起加剧了人们对AI生成文本被滥用的担忧，水印技术因此成为一种颇具前景的解决方案。当前主流的LLM水印方案分为两类：基于logits（对数概率）的方案和基于采样的方案，但现有方案在鲁棒性、文本质量与安全性之间存在权衡关系。为缓解这一问题，我们将两类方案相结合，发挥各自优势以实现协同效应。本文提出一种具备三种策略的通用共生水印框架：串行、并行与混合策略。其中混合框架利用token熵与语义熵自适应嵌入水印，优化可检测性、鲁棒性、文本质量与安全性之间的平衡。此外，我们在多个数据集与模型上开展了全面实验以验证所提方法，实验结果表明，该方法优于现有基线，达到了当前最优（SOTA）性能。我们认为该框架为不同水印范式的研究提供了新思路，代码已开源至https://github.com/redwyd/SymMark。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
