CATMark: A Context-Aware Thresholding Framework for Robust Cross-Task Watermarking in Large Language Models

• URL: http://arxiv.org/abs/2510.02342v1
• Date: Sat, 27 Sep 2025 03:43:52 GMT
• Title: CATMark: A Context-Aware Thresholding Framework for Robust Cross-Task Watermarking in Large Language Models
• Authors: Yu Zhang, Shuliang Liu, Xu Yang, Xuming Hu,
• Abstract summary: We propose a novel framework that dynamically adjusts watermarking intensity based on real-time semantic context.<n>$myalgo$ partitions text generation into semantic states using logits clustering, establishing context-aware entropy thresholds.<n>Experiments show $myalgo$ improves text quality in cross-tasks without sacrificing detection accuracy.
• Score: 37.67547464259489
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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