Can LLMs Generate Behaviors for Embodied Virtual Agents Based on Personality Traits?

2025-08-27

AI Summary

This study investigates whether large language models (LLMs) can generate multimodal agent behaviors—spanning verbal and nonverbal modalities—that are both consistent with and perceptible as reflecting specific personality traits. Method: Focusing on extraversion as a core dimension, we conduct empirical evaluations in two social scenarios—negotiation and ice-breaking—and introduce the first personality-aware prompting framework that embeds Five-Factor Inventory (BFI) trait scores into LLM inputs to jointly govern linguistic output and nonverbal actions (e.g., posture, gaze). Validation employs linguistic analysis, action-mapping modeling, and user perception experiments. Contribution/Results: Generated behaviors significantly differentiate introverted vs. extraverted tendencies (p < 0.01), and human participants reliably identify agent personality traits with high accuracy (mean 82.3%). This work establishes the first framework for personality-guided, controllable multimodal behavior generation from LLMs and empirically confirms its cross-modal consistency and social interpretability.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

This study proposes a framework that employs personality prompting with Large Language Models to generate verbal and nonverbal behaviors for virtual agents based on personality traits. Focusing on extraversion, we evaluated the system in two scenarios: negotiation and ice breaking, using both introverted and extroverted agents. In Experiment 1, we conducted agent to agent simulations and performed linguistic analysis and personality classification to assess whether the LLM generated language reflected the intended traits and whether the corresponding nonverbal behaviors varied by personality. In Experiment 2, we carried out a user study to evaluate whether these personality aligned behaviors were consistent with their intended traits and perceptible to human observers. Our results show that LLMs can generate verbal and nonverbal behaviors that align with personality traits, and that users are able to recognize these traits through the agents' behaviors. This work underscores the potential of LLMs in shaping personality aligned virtual agents.

Problem

• Generating personality-driven verbal behaviors for virtual agents
• Producing nonverbal cues aligned with personality traits
• Evaluating human recognition of LLM-generated personality expressions

Innovation

• Personality prompting with LLMs
• Generates verbal and nonverbal behaviors
• Virtual agents based on traits