大型语言模型版权保护：方法、挑战与趋势综述

Copyright Protection for Large Language Models: A Survey of Methods, Challenges, and Trends

August 15, 2025

作者: Zhenhua Xu, Xubin Yue, Zhebo Wang, Qichen Liu, Xixiang Zhao, Jingxuan Zhang, Wenjun Zeng, Wengpeng Xing, Dezhang Kong, Changting Lin, Meng Han

cs.AI

摘要

鉴于大语言模型高昂的开发成本、专有价值和潜在的滥用风险，其版权保护至关重要。现有研究主要集中于追踪大语言模型生成内容的技术——即文本水印——而对保护模型本身的方法（如模型水印和模型指纹）的系统性探讨尚属空白。此外，文本水印、模型水印与模型指纹之间的关系与区别尚未得到全面厘清。本文对大语言模型版权保护技术的现状进行了全面综述，重点聚焦于模型指纹，涵盖以下方面：（1）阐明从文本水印到模型水印及指纹的概念联系，并采用统一术语，将模型水印纳入更广泛的指纹框架；（2）概述并比较多种文本水印技术，指出这些方法在某些情况下可作为模型指纹使用的情形；（3）系统分类并比较现有用于大语言模型版权保护的模型指纹方法；（4）首次提出指纹转移与指纹移除技术；（5）总结模型指纹的评估指标，包括有效性、无害性、鲁棒性、隐蔽性和可靠性；（6）探讨开放挑战与未来研究方向。本综述旨在为研究人员提供对大语言模型时代下文本水印与模型指纹技术的深入理解，从而推动其知识产权保护的进一步进展。

English

Copyright protection for large language models is of critical importance, given their substantial development costs, proprietary value, and potential for misuse. Existing surveys have predominantly focused on techniques for tracing LLM-generated content-namely, text watermarking-while a systematic exploration of methods for protecting the models themselves (i.e., model watermarking and model fingerprinting) remains absent. Moreover, the relationships and distinctions among text watermarking, model watermarking, and model fingerprinting have not been comprehensively clarified. This work presents a comprehensive survey of the current state of LLM copyright protection technologies, with a focus on model fingerprinting, covering the following aspects: (1) clarifying the conceptual connection from text watermarking to model watermarking and fingerprinting, and adopting a unified terminology that incorporates model watermarking into the broader fingerprinting framework; (2) providing an overview and comparison of diverse text watermarking techniques, highlighting cases where such methods can function as model fingerprinting; (3) systematically categorizing and comparing existing model fingerprinting approaches for LLM copyright protection; (4) presenting, for the first time, techniques for fingerprint transfer and fingerprint removal; (5) summarizing evaluation metrics for model fingerprints, including effectiveness, harmlessness, robustness, stealthiness, and reliability; and (6) discussing open challenges and future research directions. This survey aims to offer researchers a thorough understanding of both text watermarking and model fingerprinting technologies in the era of LLMs, thereby fostering further advances in protecting their intellectual property.