  The advancement of Large Language Models (LLMs) has led to increasing
concerns about the misuse of AI-generated text, and watermarking for
LLM-generated text has emerged as a potential solution. However, it is
challenging to generate high-quality watermarked text while maintaining strong
security, robustness, and the ability to detect watermarks without prior
knowledge of the prompt or model. This paper proposes an adaptive watermarking
strategy to address this problem. To improve the text quality and maintain
robustness, we adaptively add watermarking to token distributions with high
entropy measured using an auxiliary model and keep the low entropy token
distributions untouched. For the sake of security and to further minimize the
watermark's impact on text quality, instead of using a fixed green/red list
generated from a random secret key, which can be vulnerable to decryption and
forgery, we adaptively scale up the output logits in proportion based on the
semantic embedding of previously generated text using a well designed semantic
mapping model. Our experiments involving various LLMs demonstrate that our
approach achieves comparable robustness performance to existing watermark
methods. Additionally, the text generated by our method has perplexity
comparable to that of \emph{un-watermarked} LLMs while maintaining security
even under various attacks.
