Tianle Liu, Zhiliang Tian, Zhen Huang, Tianlun Liu, Jingyuan Huang, Zhaoning Zhang, Chengcheng Shao, Dongsheng Li. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Tianlun Liu, Zhiliang Tian, Zhen Huang, Xingzhi Zhou, Wanlong Yu, Tianle Liu, Feng Liu, Dongsheng Li. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Qiunan Du, Zhiliang Tian, Zhen Huang, Kailun Bian, Tianlun Liu, Zhaoning Zhang, Xinwang Liu, Feng Liu, Dongsheng Li. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

Watermarking enables people to determine whether the text is generated by a specific model.It injects a unique signature based on the "green-red" list that can be tracked during detection, where the words in green lists are encouraged to be generated.Recent researchers propose to fix the green/red lists or increase the proportion of green tokens to defend against paraphrasing attacks.However, these methods cause degradation of text quality due to semantic disparities between the watermarked text and the unwatermarked text.In this paper, we propose a semantic-aware watermark method that considers contexts to generate a semantic-aware key to split a semantically balanced green/red list for watermark injection.The semantic balanced list reduces the performance drop due to adding bias on green lists.To defend against paraphrasing attacks, we generate the watermark key considering the semantics of contexts via locally sensitive hashing.To improve the text quality, we propose to split green/red lists considering semantics to enable the green list to cover almost all semantics.We also dynamically adapt the bias to balance text quality and robustness.The experiments show our advantages in both robustness and text quality comparable to existing baselines.