From Trade-off to Synergy: A Versatile Symbiotic Watermarking Framework
  for Large Language Models
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}.
6829e74e54b087448a2805c5
Yidan Wang
claimed_verified
2025-05-18T19:39:08.834Z
654da9621aeef70f9af2e7cb
https://cdn-avatars.huggingface.co/v1/production/uploads/654da9621aeef70f9af2e7cb/kZ8D3isElQ_hmxbzZnmVw.jpeg
Yidan Wang
user
redwyd
6829e74e54b087448a2805c6
Yubing Ren
6829e74e54b087448a2805c7
Yanan Cao
6829e74e54b087448a2805c8
Binxing Fang
From Trade-off to Synergy: A Versatile Symbiotic Watermarking Framework
  for Large Language Models
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}.
A symbiotic watermarking framework for Large Language Models integrates logits-based and sampling-based methods to balance robustness, text quality, and security, achieving state-of-the-art performance.
Large Language Models LLMs watermarking logits-based sampling-based token entropy semantic entropy serial parallel hybrid state-of-the-art