Large Language Model-based Voice Assistants (LLM-VAs) are increasingly deployed in assistive settings for older adults, yet little is known about how an agent's personality shapes user perceptions of its explanations. This paper presents a mixed factorial experiment (N=140) examining how agreeableness and extraversion in an LLM-VA ("Robin") influence older adults' perceptions across seven measures: empathy, likeability, trust, reliance, satisfaction, intention to adopt, and perceived intelligence. Results reveal that high agreeableness drove stronger empathy perceptions, while low agreeableness consistently penalized likeability. Importantly, perceived intelligence remained unaffected by personality, suggesting that personality shapes sociability without altering competence perceptions. Real-time environmental explanations outperformed conversational history explanations on five measures, with advantages concentrated in emergency contexts. Notably, highly agreeable participants were especially critical of low-agreeableness agents, revealing a user-agent personality congruence effect. These findings offer design implications for personality-aware, context-sensitive LLM-VAs in assistive settings.

翻译：基于大型语言模型的语音助手（LLM-VAs）正日益应用于老年人的辅助场景，但关于智能体个性如何影响用户对其解释的感知，目前知之甚少。本文通过一项混合因子实验（N=140），考察了LLM-VA（"Robin"）的亲和性与外向性如何影响老年人在七个维度上的感知：共情能力、好感度、信任度、依赖性、满意度、采纳意愿以及感知智能。结果表明，高亲和性显著增强了共情能力的感知，而低亲和性则持续降低了用户的好感度。重要的是，感知智能并未受到个性的影响，这表明个性塑造了社交属性，但并未改变用户对其能力的判断。在五个维度上，实时环境解释的表现均优于对话历史解释，且这一优势在紧急情境中尤为突出。值得注意的是，高亲和性参与者对低亲和性智能体的评价尤为严苛，这揭示了一种用户-智能体个性一致性效应。这些发现为在辅助场景中设计具有个性感知和情境敏感性的LLM-VAs提供了重要启示。