title: From Trade-off to Synergy: A Versatile Symbiotic Watermarking Framework
    for Large Language Models
summary: 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}.
paper_authors._id: 6829e74e54b087448a2805c5
paper_authors.name: Yidan Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-18T19:39:08.834Z
paper_authors._id: 654da9621aeef70f9af2e7cb
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/654da9621aeef70f9af2e7cb/kZ8D3isElQ_hmxbzZnmVw.jpeg
paper_authors.fullname: Yidan Wang
paper_authors.type: user
paper_authors.user: redwyd
paper_authors._id: 6829e74e54b087448a2805c6
paper_authors.name: Yubing Ren
paper_authors._id: 6829e74e54b087448a2805c7
paper_authors.name: Yanan Cao
paper_authors._id: 6829e74e54b087448a2805c8
paper_authors.name: Binxing Fang
paper_title: From Trade-off to Synergy: A Versatile Symbiotic Watermarking Framework
    for Large Language Models
paper_summary: 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}.
paper_ai_summary: 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.
paper_ai_keywords: Large Language Models LLMs watermarking logits-based sampling-based token entropy semantic entropy serial parallel hybrid state-of-the-art