The influence of persona and conversational task on social interactions with a LLM-controlled embodied conversational agent

• Main Authors: Kroczek, Leon O. H, May, Alexander, Hettenkofer, Selina, Ruider, Andreas, Ludwig, Bernd, Mühlberger, Andreas
• Format: Journal Article
• Language: English
• Published: 08-11-2024
• Subjects: Computer Science - Artificial Intelligence; Computer Science - Human-Computer Interaction
• DOI: 10.48550/arxiv.2411.05653

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.