Reinforcement Learning-based Copyright Protection Watermarking for Large Language Model

With the widespread application of large language models (LLMs) in the field of natural language processing (NLP), copyright protection issues are becoming increasingly important.As an effective means of copyright protection, watermarking technology can help developers and users prove the copyright ownership of the model.However, existing watermarking methods struggle to optimize both watermark effectiveness and model performance simultaneously.To overcome this challenge, in this paper, we propose a backdoor watermarking method named CRMark based on Chain-of-Thought (CoT) and reinforcement learning.This method embeds backdoor symbols into the prompt of the datasets and adds copyright information as the watermark into the model response.Reinforcement learning is further employed to alleviate the performance degradation of the watermark model in normal tasks.Experimental results show that the CRMark does not reduce the model's original task performance while effectively maintaining the effectiveness of backdoor watermarks, with the watermark success rate of up to 96.5%.