@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Identifying Power Relations in Conversations using Multi-Agent Social Reasoning
%A Wu, Zhaoqing
%A Goldwasser, Dan
%A Pacheco, Maria Leonor
%A Morgenstern, Leora
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S 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)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-190-2
%F wu-etal-2025-identifying
%X 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.
%R 10.18653/v1/2025.naacl-short.72
%U https://aclanthology.org/2025.naacl-short.72/
%U https://doi.org/10.18653/v1/2025.naacl-short.72
%P 855-865
Markdown (Informal)
[Identifying Power Relations in Conversations using Multi-Agent Social Reasoning](https://aclanthology.org/2025.naacl-short.72/) (Wu et al., NAACL 2025)
ACL
- Zhaoqing Wu, Dan Goldwasser, Maria Leonor Pacheco, and Leora Morgenstern. 2025. Identifying Power Relations in Conversations using Multi-Agent Social Reasoning. In 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), pages 855–865, Albuquerque, New Mexico. Association for Computational Linguistics.