  Creating human-like large language model (LLM) agents is crucial for faithful
social simulation. Having LLMs role-play based on demographic information
sometimes improves human likeness but often does not. This study assessed
whether LLM alignment with human behavior can be improved by integrating
information from empirically-derived human belief networks. Using data from a
human survey, we estimated a belief network encompassing 18 topics loading on
two non-overlapping latent factors. We then seeded LLM-based agents with an
opinion on one topic, and assessed the alignment of its expressed opinions on
remaining test topics with corresponding human data. Role-playing based on
demographic information alone did not align LLM and human opinions, but seeding
the agent with a single belief greatly improved alignment for topics related in
the belief network, and not for topics outside the network. These results
suggest a novel path for human-LLM belief alignment in work seeking to simulate
and understand patterns of belief distributions in society.
