@inproceedings{vijjini-etal-2026-llm,
    title = "Do {LLM} Agents Mirror Socio-Cognitive Effects in Power-Asymmetric Conversations?",
    author = "Vijjini, Anvesh Rao  and
      Manjunath, Sagar B.  and
      Chaturvedi, Snigdha",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.2202/",
    pages = "47676--47701",
    ISBN = "979-8-89176-390-6",
    abstract = "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."
}