AI-Press: A Multi-Agent News Generating and Feedback Simulation System Powered by Large Language Models

Xiawei Liu, Shiyue Yang, Xinnong Zhang, Haoyu Kuang, Libo Sun, Yihang Yang, Siming Chen, Xuanjing Huang, Zhongyu Wei


Abstract
We introduce AI-Press, an automated news drafting and polishing system based on multi-agent collaboration and Retrieval-Augmented Generation. We develop a feedback simulation system that generates public responses considering demographic distributions. Demo link: https://youtu.be/TmjfJrbzaRU
Anthology ID:
2025.coling-demos.8
Volume:
Proceedings of the 31st International Conference on Computational Linguistics: System Demonstrations
Month:
January
Year:
2025
Address:
Abu Dhabi, UAE
Editors:
Owen Rambow, Leo Wanner, Marianna Apidianaki, Hend Al-Khalifa, Barbara Di Eugenio, Steven Schockaert, Brodie Mather, Mark Dras
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
63–82
Language:
URL:
https://aclanthology.org/2025.coling-demos.8/
DOI:
Bibkey:
Cite (ACL):
Xiawei Liu, Shiyue Yang, Xinnong Zhang, Haoyu Kuang, Libo Sun, Yihang Yang, Siming Chen, Xuanjing Huang, and Zhongyu Wei. 2025. AI-Press: A Multi-Agent News Generating and Feedback Simulation System Powered by Large Language Models. In Proceedings of the 31st International Conference on Computational Linguistics: System Demonstrations, pages 63–82, Abu Dhabi, UAE. Association for Computational Linguistics.
Cite (Informal):
AI-Press: A Multi-Agent News Generating and Feedback Simulation System Powered by Large Language Models (Liu et al., COLING 2025)
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PDF:
https://aclanthology.org/2025.coling-demos.8.pdf