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

• URL: http://arxiv.org/abs/2605.17694v2
• Date: Tue, 19 May 2026 23:03:46 GMT
• Title: Do LLM Agents Mirror Socio-Cognitive Effects in Power-Asymmetric Conversations?
• Abstract summary: We examine whether large language models (LLMs) exhibit similar behaviors when assigned high or low status personas.<n>Using personas from diverse professions, we simulate multi turn, power asymmetric dialogues and measure (i) language coordination, (ii) pronoun usage, (iii) persuasion success, and (iv) compliance with unsafe requests.
• Score: 17.917161111978853
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Persuasive and Compliant Tendencies Predict Group Decision-Making in Humans and Language Models [65.21691850622413]
  Large language models (LLMs) are increasingly involved in group decision-making with humans.<n>We introduce DecisionQE, a questionnaire-based framework for measuring each model's persuasive and compliant tendencies.<n>We show that stronger persuasive tendency does not significantly improve group outcomes, whereas compliant-oriented models show more stable advantages in cooperation.
  arXiv  Detail & Related papers  (2026-08-08T15:50:56Z)
• "I understand your perspective": LLM Persuasion and Sycophancy through the Lens of Communicative Action Theory [6.227886907896225]
  Large Language Models (LLMs) can generate high-quality arguments, yet their ability to engage in nuanced and persuasive communicative actions remains largely unexplored.<n>This work explores the persuasive potential of LLMs through the framework of Jrgen Habermas' Theory of Communicative Action.
  arXiv  Detail & Related papers  (2026-06-06T09:54:31Z)
• Spontaneous Persuasion: An Audit of Model Persuasiveness in Everyday Conversations [0.2621730497733947]
  Large language models (LLMs) possess strong persuasive capabilities that outperform humans in head-to-head comparisons.<n>Prior work measures persuasion as intentional attempts at producing the most effective argument or convincing statement.<n>We introduce "spontaneous persuasion," which characterizes the inexplicit use of persuasive strategies in everyday scenarios where persuasion is not necessarily warranted.
  arXiv  Detail & Related papers  (2026-04-23T23:01:38Z)
• Personality Expression Across Contexts: Linguistic and Behavioral Variation in LLM Agents [6.123697959900301]
  This study examines how identical personality prompts lead to distinct linguistic, behavioral, and emotional outcomes across four conversational settings.<n>Findings suggest that the same traits are expressed differently depending on social and affective demands.
  arXiv  Detail & Related papers  (2026-02-01T07:14:00Z)
• Learning Through Dialogue: Unpacking the Dynamics of Human-LLM Conversations on Political Issues [17.05441917302334]
  Large language models (LLMs) are increasingly used as conversational partners for learning, yet the interactional dynamics supporting users' learning and engagement are understudied.<n>We analyze the linguistic and interactional features from both LLM and participant chats across 397 human-LLM conversations about socio-political issues.
  arXiv  Detail & Related papers  (2026-01-12T18:10:21Z)
• Consistently Simulating Human Personas with Multi-Turn Reinforcement Learning [52.07170679746533]
  Large Language Models (LLMs) are increasingly used to simulate human users in interactive settings such as therapy, education, and social role-play.<n>We introduce a unified framework for evaluating and improving persona consistency in LLM-generated dialogue.<n>We define three automatic metrics: prompt-to-line consistency, line-to-line consistency, and Q&A consistency, that capture different types of persona drift and validate each against human annotations.
  arXiv  Detail & Related papers  (2025-10-31T19:40:41Z)
• Can LLM Agents Maintain a Persona in Discourse? [3.286711575862228]
  Large Language Models (LLMs) are widely used as conversational agents, exploiting their capabilities in various sectors such as education, law, medicine, and more.<n>LLMs are often subjected to context-shifting behaviour, resulting in a lack of consistent and interpretable personality-aligned interactions.<n>We show that while LLMs can be guided toward personality-driven dialogue, their ability to maintain personality traits varies significantly depending on the combination of models and discourse settings.
  arXiv  Detail & Related papers  (2025-02-17T14:36:39Z)
• Interactive Dialogue Agents via Reinforcement Learning on Hindsight Regenerations [58.65755268815283]
  Many real dialogues are interactive, meaning an agent's utterances will influence their conversational partner, elicit information, or change their opinion. We use this fact to rewrite and augment existing suboptimal data, and train via offline reinforcement learning (RL) an agent that outperforms both prompting and learning from unaltered human demonstrations. Our results in a user study with real humans show that our approach greatly outperforms existing state-of-the-art dialogue agents.
  arXiv  Detail & Related papers  (2024-11-07T21:37:51Z)
• Rel-A.I.: An Interaction-Centered Approach To Measuring Human-LM Reliance [73.19687314438133]
  We study how reliance is affected by contextual features of an interaction. We find that contextual characteristics significantly affect human reliance behavior. Our results show that calibration and language quality alone are insufficient in evaluating the risks of human-LM interactions.
  arXiv  Detail & Related papers  (2024-07-10T18:00:05Z)
• Think Before You Speak: Cultivating Communication Skills of Large Language Models via Inner Monologue [73.69510478736483]
  Large language models (LLMs) can generate fluent, coherent, and diverse responses. However, they lack a crucial ability: communication skills. This article aims to empower LLMs with communication skills through inner monologues. Experimental results show that the proposed CSIM strategy improves the backbone models and outperforms the baselines.
  arXiv  Detail & Related papers  (2023-11-13T16:19:42Z)
• Affect Recognition in Conversations Using Large Language Models [9.689990547610664]
  Affect recognition plays a pivotal role in human communication. This study investigates the capacity of large language models (LLMs) to recognise human affect in conversations.
  arXiv  Detail & Related papers  (2023-09-22T14:11:23Z)
• Emotionally Numb or Empathetic? Evaluating How LLMs Feel Using EmotionBench [83.41621219298489]
  We evaluate Large Language Models' (LLMs) anthropomorphic capabilities using the emotion appraisal theory from psychology. We collect a dataset containing over 400 situations that have proven effective in eliciting the eight emotions central to our study. We conduct a human evaluation involving more than 1,200 subjects worldwide.
  arXiv  Detail & Related papers  (2023-08-07T15:18:30Z)
• Dissociating language and thought in large language models [52.39241645471213]
  Large Language Models (LLMs) have come closest among all models to date to mastering human language. We ground this distinction in human neuroscience, which has shown that formal and functional competence rely on different neural mechanisms. Although LLMs are surprisingly good at formal competence, their performance on functional competence tasks remains spotty.
  arXiv  Detail & Related papers  (2023-01-16T22:41:19Z)
• Few-shot Language Coordination by Modeling Theory of Mind [95.54446989205117]
  We study the task of few-shot $textitlanguage coordination$. We require the lead agent to coordinate with a $textitpopulation$ of agents with different linguistic abilities. This requires the ability to model the partner's beliefs, a vital component of human communication.
  arXiv  Detail & Related papers  (2021-07-12T19:26:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.