Vibe Check: 探讨基于大语言模型的对话代理的性格和对齐对用户在目标导向任务中的感知影响
Vibe Check: Understanding the Effects of LLM-Based Conversational Agents' Personality and Alignment on User Perceptions in Goal-Oriented Tasks
AI总结：
本研究探讨了对话代理性格表达水平和用户与代理性格对齐如何影响用户在目标导向任务中的感知，发现中等表达水平在多个维度上表现最佳，性格对齐进一步提升了结果，尤其在外向性和情绪稳定性和方面影响显著。
摘要：
大型语言模型（LLMs）使对话代理（CAs）能够表达独特的个性，引发了关于此类设计如何塑造用户感知的新问题。本研究探讨了性格表达水平和用户-代理性格对齐如何影响目标导向任务中的感知。在一项被试间实验（N=150）中，参与者与表现出低、中或高表达水平（基于大五特质）的CAs完成了旅行规划任务，通过我们的新Trait Modulation Keys框架进行控制。结果揭示出倒U型关系：中等表达水平在智力、乐趣、拟人化、采用意愿、信任和喜爱度等方面产生了最积极的评价，显著优于极端情况。性格对齐进一步提升了结果，外向性和情绪稳定性成为最影响的特质。聚类分析识别出三种不同的兼容性轮廓，
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
