Beyond Demographics: Aligning Role-playing LLM-based Agents Using Human Belief Networks
<|reference_start|>Beyond Demographics: Aligning Role-playing LLM-based Agents Using Human Belief Networks: 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.<|reference_end|>
@article{chuang2024beyond,
    title={Beyond Demographics: Aligning Role-playing LLM-based Agents Using Human
  Belief Networks},
    author={Yun-Shiuan Chuang, Krirk Nirunwiroj, Zach Studdiford, Agam Goyal,
  Vincent V. Frigo, Sijia Yang, Dhavan Shah, Junjie Hu, Timothy T. Rogers},
    journal={Findings of the Association for Computational Linguistics (ACL):
  EMNLP 2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.17232},
    primaryClass={cs.CL}
}