From Trade-off to Synergy: A Versatile Symbiotic Watermarking Framework
  for Large Language Models
Authors: name: Yidan Wang, name: Yubing Ren, name: Yanan Cao, name: Binxing Fang

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{https://github.com/redwyd/SymMark}.