Problem
Research questions and friction points this paper is trying to address.
large language models
sustained exchanges
personality-verified
talk-to-listen ratio
problem-solving
All four leading LLMs talk more than they listen to personality-verified synthetic help-seekers
🤖 AI Summary
研究针对大型语言模型在危机咨询中过度说话的问题，通过构建人格意识评估方法，让模型与具有特定心理特征的虚拟求助者对话，发现模型普遍表现出话多且急于解决问题的现象。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
personality-aware evaluation
psychometrically specified profile
Big Five and beyond
talk-to-listen ratio
emotion stabilisation
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