Personalized Large Language Models Can Increase the Belief Accuracy of Social Networks

Personalized LLMs can help correct false beliefs: The study found that when people interacted with large language models tailored to their individual profiles, they updated their beliefs to be more accurate, suggesting these tools have genuine potential to combat misinformation rather than simply reinforce existing views.

The network effect amplifies accuracy gains: Beyond individual belief correction, people who had access to a personalized LLM in their social network actively chose to connect with others who held more accurate beliefs (similar to the LLM's positions), meaning the corrective impact spread through social networks rather than stopping at individual users.

People voluntarily engage with accurate AI sources: Study participants indicated they would continue seeking information from the personalized LLM in future interactions, demonstrating that people don't necessarily reject AI-provided corrections when the information is accurate and presented in a personalized way.

Design matters for responsible AI deployment: The LLMs in this study were specifically constructed with "guardrails for accurate information retrieval" and personalization features, suggesting that thoughtful design choices—not just the technology itself—determine whether AI tools help or harm public understanding on contested issues.

Social network structure can be reshaped by AI: This research reveals that LLMs don't just influence what individuals believe; they can reshape the social networks people build, which has significant implications for how misinformation or accurate information spreads at scale.