LLMs are Introvert
Journal:
arXiv
Published Date:
Jul 8, 2025
Abstract
The exponential growth of social media and generative AI has transformed
information dissemination, fostering connectivity but also accelerating the
spread of misinformation. Understanding information propagation dynamics and
developing effective control strategies is essential to mitigate harmful
content. Traditional models, such as SIR, provide basic insights but
inadequately capture the complexities of online interactions. Advanced methods,
including attention mechanisms and graph neural networks, enhance accuracy but
typically overlook user psychology and behavioral dynamics. Large language
models (LLMs), with their human-like reasoning, offer new potential for
simulating psychological aspects of information spread. We introduce an
LLM-based simulation environment capturing agents' evolving attitudes,
emotions, and responses. Initial experiments, however, revealed significant
gaps between LLM-generated behaviors and authentic human dynamics, especially
in stance detection and psychological realism. A detailed evaluation through
Social Information Processing Theory identified major discrepancies in
goal-setting and feedback evaluation, stemming from the lack of emotional
processing in standard LLM training. To address these issues, we propose the
Social Information Processing-based Chain of Thought (SIP-CoT) mechanism
enhanced by emotion-guided memory. This method improves the interpretation of
social cues, personalization of goals, and evaluation of feedback. Experimental
results confirm that SIP-CoT-enhanced LLM agents more effectively process
social information, demonstrating behaviors, attitudes, and emotions closer to
real human interactions. In summary, this research highlights critical
limitations in current LLM-based propagation simulations and demonstrates how
integrating SIP-CoT and emotional memory significantly enhances the social
intelligence and realism of LLM agents.
