Sari la conținut

Română · beta. Traducerea a fost făcută automat și este încă în curs de revizuire. Citește pagina în engleză

perspectiva aiqbenchSiguranță AI

analiza aiqbench

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.
Sursă care acoperăarXiv
Ce este aceastaLectura unui model asupra articolului și a acoperirii conexe, scrisă pentru aiqbench. Nu sunt cuvintele publicației.

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

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

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

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Text integral sub licența deschisă a publicației · 199 cuvinte

Subiecte conexe