调适对话机器人性格表达，能显著提升用户信任与体验。
Vibe Check: Understanding the Effects of LLM-Based Conversational Agents' Personality and Alignment on User Perceptions in Goal-Oriented Tasks
- 用新框架控制五大性格特质的表达强度
- 中等性格表达获最高评分，极端表达效果差
- 外向和情绪稳定最影响用户好感，适合个性化设计
大型语言模型（LLMs）使对话代理（CAs）具备独特人格特征，引发关于其如何影响用户感知的新问题。本研究探究了性格表达水平与用户-代理性格匹配度在目标导向任务中的影响。在一项被试间实验（N=150）中，参与者与在五大性格特质上表现出低、中、高表达水平的CA完成旅行规划任务，表达水平通过我们提出的新型特质调节键（Trait Modulation Keys）框架控制。结果揭示出倒U型关系：中等表达在智能感、愉悦感、拟人化、采纳意愿、信任度和喜爱度方面均获得最优评价，显著优于两极。性格匹配进一步提升效果，其中外向性和情绪稳定性影响最大。聚类分析识别出三种不同匹配类型，'高度匹配'用户感知显著更积极。研究表明，性格表达与战略性特质对齐是优化对话代理设计的关键，为日益普及的基于LLM的对话代理提供了设计启示。
原文摘要 · Abstract (English)
Large language models (LLMs) enable conversational agents (CAs) to express distinctive personalities, raising new questions about how such designs shape user perceptions. This study investigates how personality expression levels and user-agent personality alignment influence perceptions in goal-oriented tasks. In a between-subjects experiment (N=150), participants completed travel planning with CAs exhibiting low, medium, or high expression across the Big Five traits, controlled via our novel Trait Modulation Keys framework. Results revealed an inverted-U relationship: medium expression produced the most positive evaluations across Intelligence, Enjoyment, Anthropomorphism, Intention to Adopt, Trust, and Likeability, significantly outperforming both extremes. Personality alignment further enhanced outcomes, with Extraversion and Emotional Stability emerging as the most influential traits. Cluster analysis identified three distinct compatibility profiles, with "Well-Aligned" users reporting substantially positive perceptions. These findings demonstrate that personality expression and strategic trait alignment constitute optimal design targets for CA personality, offering design implications as LLM-based CAs become increasingly prevalent.
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