Large Language Models (LLMs) have demonstrated remarkable capabilities in conversational tasks. Embodying an LLM as a virtual human allows users to engage in face-to-face social interactions in Virtual Reality. However, the influence of person- and task-related factors in social interactions with LLM-controlled agents remains unclear. In this study, forty-six participants interacted with a virtual agent whose persona was manipulated as extravert or introvert in three different conversational tasks (small talk, knowledge test, convincing). Social-evaluation, emotional experience, and realism were assessed using ratings. Interactive engagement was measured by quantifying participants' words and conversational turns. Finally, we measured participants' willingness to ask the agent for help during the knowledge test. Our findings show that the extraverted agent was more positively evaluated, elicited a more pleasant experience and greater engagement, and was assessed as more realistic compared to the introverted agent. Whereas persona did not affect the tendency to ask for help, participants were generally more confident in the answer when they had help of the LLM. Variation of personality traits of LLM-controlled embodied virtual agents, therefore, affects social-emotional processing and behavior in virtual interactions. Embodied virtual agents allow the presentation of naturalistic social encounters in a virtual environment.

翻译：大语言模型（LLMs）在对话任务中展现出卓越的能力。将大语言模型具身为虚拟人，使用户能够在虚拟现实中参与面对面的社交互动。然而，在与LLM控制的代理进行社交互动时，人格相关因素和任务相关因素的影响尚不明确。本研究招募了四十六名参与者，与一个虚拟代理进行互动，该代理的人格被操纵为外向型或内向型，并在三种不同的对话任务（闲谈、知识测试、说服任务）中进行。通过评分评估了社交评价、情感体验和真实感。通过量化参与者的言语数量和对话轮次来测量交互参与度。最后，我们测量了参与者在知识测试中向代理寻求帮助的意愿。研究结果表明，与内向型代理相比，外向型代理获得了更积极的评价，引发了更愉悦的体验和更高的参与度，并被评估为更真实。虽然人格并未影响求助倾向，但当参与者获得LLM的帮助时，他们普遍对答案更有信心。因此，LLM控制的具身虚拟代理的人格特质变化会影响虚拟互动中的社会情感处理和行为。具身虚拟代理使得在虚拟环境中呈现自然主义的社交遭遇成为可能。