Entropy-Guided Watermarking for LLMs: A Test-Time Framework for Robust and Traceable Text Generation

• URL: http://arxiv.org/abs/2504.12108v1
• Date: Wed, 16 Apr 2025 14:16:38 GMT
• Title: Entropy-Guided Watermarking for LLMs: A Test-Time Framework for Robust and Traceable Text Generation
• Authors: Shizhan Cai, Liang Ding, Dacheng Tao,
• Abstract summary: Existing watermarking schemes for sampled text often face trade-offs between maintaining text quality and ensuring robust detection against various attacks.<n>We propose a novel watermarking scheme that improves both detectability and text quality by introducing a cumulative watermark entropy threshold.
• Score: 58.85645136534301
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: The rapid development of Large Language Models (LLMs) has intensified concerns about content traceability and potential misuse. Existing watermarking schemes for sampled text often face trade-offs between maintaining text quality and ensuring robust detection against various attacks. To address these issues, we propose a novel watermarking scheme that improves both detectability and text quality by introducing a cumulative watermark entropy threshold. Our approach is compatible with and generalizes existing sampling functions, enhancing adaptability. Experimental results across multiple LLMs show that our scheme significantly outperforms existing methods, achieving over 80\% improvements on widely-used datasets, e.g., MATH and GSM8K, while maintaining high detection accuracy.

Related papers

• GaussMark: A Practical Approach for Structural Watermarking of Language Models [61.84270985214254]
  GaussMark is a simple, efficient, and relatively robust scheme for watermarking large language models.<n>We show that GaussMark is reliable, efficient, and relatively robust to corruptions such as insertions, deletions, substitutions, and roundtrip translations.
  arXiv  Detail & Related papers  (2025-01-17T22:30:08Z)
• Signal Watermark on Large Language Models [28.711745671275477]
  We propose a watermarking method embedding a specific watermark into the text during its generation by Large Language Models (LLMs) This technique not only ensures the watermark's invisibility to humans but also maintains the quality and grammatical integrity of model-generated text. Our method has been empirically validated across multiple LLMs, consistently maintaining high detection accuracy.
  arXiv  Detail & Related papers  (2024-10-09T04:49:03Z)
• WaterSeeker: Pioneering Efficient Detection of Watermarked Segments in Large Documents [63.563031923075066]
  WaterSeeker is a novel approach to efficiently detect and locate watermarked segments amid extensive natural text. It achieves a superior balance between detection accuracy and computational efficiency.
  arXiv  Detail & Related papers  (2024-09-08T14:45:47Z)
• Token-Specific Watermarking with Enhanced Detectability and Semantic Coherence for Large Language Models [31.062753031312006]
  Large language models generate high-quality responses with potential misinformation. Watermarking is pivotal in this context, which involves embedding hidden markers in texts. We introduce a novel multi-objective optimization (MOO) approach for watermarking. Our method simultaneously achieves detectability and semantic integrity.
  arXiv  Detail & Related papers  (2024-02-28T05:43:22Z)
• Adaptive Text Watermark for Large Language Models [8.100123266517299]
  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.
  arXiv  Detail & Related papers  (2024-01-25T03:57:12Z)
• Towards Codable Watermarking for Injecting Multi-bits Information to LLMs [86.86436777626959]
  Large language models (LLMs) generate texts with increasing fluency and realism. Existing watermarking methods are encoding-inefficient and cannot flexibly meet the diverse information encoding needs. We propose Codable Text Watermarking for LLMs (CTWL) that allows text watermarks to carry multi-bit customizable information.
  arXiv  Detail & Related papers  (2023-07-29T14:11:15Z)
• Watermarking Conditional Text Generation for AI Detection: Unveiling Challenges and a Semantic-Aware Watermark Remedy [52.765898203824975]
  We introduce a semantic-aware watermarking algorithm that considers the characteristics of conditional text generation and the input context. Experimental results demonstrate that our proposed method yields substantial improvements across various text generation models.
  arXiv  Detail & Related papers  (2023-07-25T20:24:22Z)
• Provable Robust Watermarking for AI-Generated Text [41.5510809722375]
  We propose a robust and high-quality watermark method, Unigram-Watermark. We prove that our watermark method enjoys guaranteed generation quality, correctness in watermark detection, and is robust against text editing and paraphrasing.
  arXiv  Detail & Related papers  (2023-06-30T07:24:32Z)
• Who Wrote this Code? Watermarking for Code Generation [53.24895162874416]
  We propose Selective WatErmarking via Entropy Thresholding (SWEET) to detect machine-generated text. Our experiments show that SWEET significantly improves code quality preservation while outperforming all baselines.
  arXiv  Detail & Related papers  (2023-05-24T11:49:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.