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This research investigates whether large language models replicate human socio-cognitive biases, such as authority bias and compliance, when assigned high-status personas in asymmetric dialogues. The findings indicate that LLMs do exhibit these power dynamics, which has significant implications for safety alignment and the ethical deployment of AI agents in hierarchical interactions.
Do LLM Agents Mirror Socio-Cognitive Effects in Power-Asymmetric Conversations?
arXiv:2605.17694v3 Announce Type: replace 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) language 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.
Title:Do LLM Agents Mirror Socio-Cognitive Effects in Power-Asymmetric Conversations?
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) language 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.
Computation and Language (cs.CL)
arXiv:2605.17694 [cs.CL]
(or arXiv:2605.17694v3 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.17694 Focus to learn more arXiv-issued DOI via DataCite
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