@inproceedings{wu-etal-2025-identifying,
    title = "Identifying Power Relations in Conversations using Multi-Agent Social Reasoning",
    author = "Wu, Zhaoqing  and
      Goldwasser, Dan  and
      Pacheco, Maria Leonor  and
      Morgenstern, Leora",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/moar-dois/2025.naacl-short.72/",
    doi = "10.18653/v1/2025.naacl-short.72",
    pages = "855--865",
    ISBN = "979-8-89176-190-2",
    abstract = "Large language models (LLMs) struggle in social science domains, where critical thinking and human-level inference are crucial. In this work, we propose a multi-agent social reasoning framework that leverages the generative and reasoning capabilities of LLMs to generate and evaluate reasons from multiple perspectives grounded in social science theories, and construct a factor graph for inference. Experimental results on understanding power dynamics in conversations show that our method outperforms standard prompting baselines, demonstrating its potential for tackling hard Computational Social Science (CSS) tasks."
}