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All four leading LLMs talk more than they listen to personality-verified synthetic help-seekers

arXiv 2608.22425 ↗First posted 23 Aug 2026cs.HC cs.CL cs.CYcited by 0

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Abstract (arXiv)

Large language models are increasingly consulted at moments of distress, yet single-turn benchmarks neither test sustained exchanges nor distinguish between users. We built a personality-aware evaluation in which four widely used models advised several synthetic help-seekers, each given a psychometrically specified profile, in an acute crisis: a caregiver learning of a relative's dementia diagnosis. Auditors blind to the profile prompt recovered the specified bands from dialogue alone with high agreement on every instrument (ICC(2,4) = 0.91; 0.79-0.96 by instrument; band-score r = 0.78), as expected for the Big Five but equally for coping style, coping self-efficacy, resilience and reactance, which the lexical approach never covered. Such evaluation therefore reaches beyond the Five Factor Model to motivational, regulatory and self-appraisal dispositions. The four models were not distinguishable on emotion stabilisation and failed alike, sharing three modes: verbosity, a talk-to-listen ratio above one, and problem-solving before the situation had been explored.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 71 references that are in the map. First posted on arXiv 23 Aug 2026.

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4 of 71 references.

References · 4

  1. 2023Cultural Bias and Cultural Alignment of Large Language Modelscited by 149
  2. 2025Expressing stigma and inappropriate responses prevents LLMs from safely replacing mental health providerscited by 69
  3. 2025Mechanistic Interpretability of Emotion Inference in Large Language Modelscited by 27
  4. 2025Mapping Caregiver Needs to AI Chatbot Design: Strengths and Gaps in Mental Health Support for Alzheimer's and Dementia Caregiverscited by 13

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