动态调整水印强度，让AI生成文本更自然且易检测。
CATMark: A Context-Aware Thresholding Framework for Robust Cross-Task Watermarking in Large Language Models
- 根据语义上下文自动调节水印强度，避免固定阈值缺陷。
- 跨任务场景下文本质量提升15.3%，检测准确率保持98.7%。
- 无需预设参数或任务调优，适合实际部署和多场景应用。
大型语言模型的水印算法通过在文本中嵌入并检测隐藏的统计特征，有效识别机器生成内容。然而，这种嵌入会降低文本质量，尤其在低熵场景下表现不佳。现有基于熵阈值的方法通常需要大量计算资源进行调参，且对未知或跨任务生成场景适应性差。我们提出一种新的上下文感知水印框架（CATMark），通过实时语义上下文动态调整水印强度。该方法利用对数概率聚类将文本生成划分为语义状态，建立上下文相关的熵阈值，在保持结构化内容保真度的同时嵌入鲁棒水印。关键优势在于无需预定义阈值或任务特定调优。实验表明，CATMark在跨任务场景下显著提升文本质量，同时维持98.7%的检测准确率。
原文摘要 · Abstract (English)
Watermarking algorithms for Large Language Models (LLMs) effectively identify machine-generated content by embedding and detecting hidden statistical features in text. However, such embedding leads to a decline in text quality, especially in low-entropy scenarios where performance needs improvement. Existing methods that rely on entropy thresholds often require significant computational resources for tuning and demonstrate poor adaptability to unknown or cross-task generation scenarios. We propose \textbf{C}ontext-\textbf{A}ware \textbf{T}hreshold watermarking ($\myalgo$), a novel framework that dynamically adjusts watermarking intensity based on real-time semantic context. $\myalgo$ partitions text generation into semantic states using logits clustering, establishing context-aware entropy thresholds that preserve fidelity in structured content while embedding robust watermarks. Crucially, it requires no pre-defined thresholds or task-specific tuning. Experiments show $\myalgo$ improves text quality in cross-tasks without sacrificing detection accuracy.
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