Watermarking with Low-Entropy POS -Guided Token Partitioning and Z -Score-Driven Dynamic Bias for Large Language Models (2025)

Abstract: Texts generated by large language models (LLMs) are increasingly widespread online. Due to the lack of effective attribution mechanisms, the enforcement of copyright and the prevention of misuse remain significant challenges in the context of LLM-generated content. LLMs watermark emerges as a crucial technology to trace the source of AI-generated content. However, most existing watermarking methods reduce the fidelity of semantics. To address this issue, this paper introduces a novel watermarking framework. To enhance the fidelity of semantics, we propose low-entropy POS-guided token partitioning mechanism and z-score-driven dynamic bias mechanism. Moreover, to enhance the robustness against potential bias sparsity exploitation attack, we propose a relative position encoding (RPE) mechanism, which can uniformly distribute bias in the generated text. Evaluated across 6 baselines, 4 tasks, and 5 LLMs under 8 attacks, compared to the KGW, our watermark improves semantic fidelity by 24.53% (RC-PPL) and robustness by 3.75% (F1). Our code is publicly available, facilitating reproducibility in LLM watermarking research.

## introduction in recent years, large language models (llms) have made rapid progress, exemplified by models such as @xcite , @xcite , qwen2.5 @xcite . these models have been widely applied across diverse natural language processing (nlp) tasks, including text generation @xcite , long-form question answering @xcite , and story extension @xcite . as llms continue to advance in these nlp domains, critical research topics have emerged concerning copyright @xcite , privacy @xcite , and security @xcite , which have become prominent areas of investigation. watermarking @xcite @xcite has emerged as a pivotal technology for tracing the origin of text outputs @xcite . ## related work existing llm watermarking methods foot_0 can be broadly classified into two categories: logits perturbation-based methods and token samplingbased methods, as systematically summarized in ## experimental setting we conduct a comprehensive evaluation of 6 watermarking methods across 4 nlp tasks, 4 datasets, and 5 llms under 8 types of adversarial attacks. experiments are performed on two nvidia a800 (80gb) gpus, using datasets with over 500 samples each, taking up a total of around 100 gpu hours. this section details task configurations, evaluation metrics, and parameter settings.