Large Language Models (LLMs) have demonstrated remarkable capabilities, but their training requires extensive data and computational resources, rendering them valuable digital assets. Therefore, it is essential to watermark LLMs to protect their copyright and trace unauthorized use or resale. Existing methods for watermarking LLMs primarily rely on training LLMs with a watermarked dataset, which entails burdensome training costs and negatively impacts the LLM's performance. In addition, their watermarked texts are not logical or natural, thereby reducing the stealthiness of the watermark. To address these issues, we propose EditMark, the first watermarking method that leverages model editing to embed a training-free, stealthy, and performance-lossless watermark for LLMs. We observe that some questions have multiple correct answers. Therefore, we assign each answer a unique watermark and update the weights of LLMs to generate corresponding questions and answers through the model editing technique. In addition, we refine the model editing technique to align with the requirements of watermark embedding. Specifically, we introduce an adaptive multi-round stable editing strategy, coupled with the injection of a noise matrix, to improve both the effectiveness and robustness of the watermark embedding. Extensive experiments indicate that EditMark can embed 32-bit watermarks into LLMs within 20 seconds (Fine-tuning: 6875 seconds) with a watermark extraction success rate of 100%, which demonstrates its effectiveness and efficiency. External experiments further demonstrate that EditMark has fidelity, stealthiness, and a certain degree of robustness against common attacks.
Can Watermarked LLMs be Identified by Users via Crafted Prompts?
Tsinghua University（清华大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；The Chinese University of Hongkong（香港中文大学）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Hongkong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Have You Merged My Model? On The Robustness of Large Language Model IP Protection Methods Against Model Merging
Tsinghua University（清华大学）；Xidian University（西安电子科技大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Zhongguancun Laboratory（中关村实验室）；National Financial Cryptography Research Center（国家金融密码研究中心）
Large Language Model Watermark Stealing With Mixed Integer Programming
Griffith University（格里菲斯大学）；University of Technology, Sydney（悉尼科技大学）；Royal Melbourne Institute of Technology（皇家墨尔本理工大学）；Huazhong University of Science and Technology（华中科技大学）
