Post-Hoc Watermarking for Robust Detection in Text Generated by Large Language Models
Jifei Hao, Jipeng Qiang, Yi Zhu, Yun Li, Yunhao Yuan, Xiaoye Ouyang
Abstract
Research on text simplification has been ongoing for many years, yet document simplification remains a significant challenge due to the need to address complex factors such as technical terminology, metaphors, and overall coherence. In this work, we introduce a novel multi-agent framework AgentSimp for document simplification, based on large language models. This framework simulates the collaborative efforts of a team of human experts through the roles played by multiple agents, effectively meeting the intricate demands of document simplification. We investigate two communication strategies among agents (pipeline-style and synchronous) and two document reconstruction strategies (Direct and Iterative). According to both automatic evaluation metrics and human evaluation results, AgentSimp produces simplified documents that are more thoroughly simplified and more coherent across various articles and styles.
BibTeX
@inproceedings{hao-etal-2025-post,
title = "Post-Hoc Watermarking for Robust Detection in Text Generated by Large Language Models",
author = "Hao, Jifei and
Qiang, Jipeng and
Zhu, Yi and
Li, Yun and
Yuan, Yunhao and
Ouyang, Xiaoye",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.364/",
pages = "5430--5442"
}