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ACL 2026longmain

Do LLM Agents Mirror Socio-Cognitive Effects in Power-Asymmetric Conversations?

Anvesh Rao Vijjini, Sagar B. Manjunath, Snigdha Chaturvedi

Department of Computer Science, University of North Carolina at Chapel Hill

PDF 由论文原始站点提供,PaperCompass 不保存论文文件。DOI 10.18653/v1/2026.acl-long.2202 ↗

摘要

Power differences shape human communication through well-documented socio-cognitive effects, including language coordination, pronoun usage, authority bias, and harmful compliance. We examine whether large language models (LLMs) exhibit similar behaviors when assigned high- or low-status personas. Using personas from diverse professions, we simulate multi-turn, power-asymmetric dialogues (e.g., principal–teacher, justice–lawyer) and measure (i) linguistic coordination, (ii) pronoun usage, (iii) persuasion success, and (iv) compliance with unsafe requests. Our results show that LLMs show key socio-cognitive effects of power, albeit with nuances and variability, linking simulated interactions to both desirable and unsafe behaviors.