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.

翻译：摘要：大型语言模型（LLMs）使对话代理（CAs）能够表达鲜明个性，引发了关于此类设计如何塑造用户感知的新问题。本研究探讨了个性表达水平及用户-代理个性一致性对目标导向任务中感知的影响。通过一项组间实验（N=150），参与者使用基于我们提出的特质调制键框架控制的大五人格低、中、高表达水平的CA完成旅行规划任务。结果揭示了倒U型关系：中等表达水平在智力、愉悦感、拟人化、采用意向、信任和喜爱度上产生最积极评价，显著优于极端水平。个性一致性进一步增强了结果，其中外向性和情绪稳定性成为最具影响力的特质。聚类分析识别出三种不同的兼容性特征，其中“高度匹配”用户报告了显著积极的感知。这些发现表明，个性表达与特质策略性对齐构成了CA个性的最优设计目标，为基于LLM的对话代理日益普及时的设计实践提供了启示。