A semantic embedding space based on large language models for modelling human beliefs

Byunghwee Lee, Rachith Aiyappa, Yong-Yeol Ahn, Haewoon Kwak, Jisun An

Abstract: Beliefs form the foundation of human cognition and decision-making, guiding our actions and social connections. A model encapsulating beliefs and their interrelationships is crucial for understanding their influence on our actions. However, research on belief interplay has often been limited to beliefs related to specific issues and has relied heavily on surveys. Here we propose a method to study the nuanced interplay between thousands of beliefs by leveraging online user debate data and mapping beliefs onto a neural embedding space constructed using a fine-tuned large language model. This belief space captures the interconnectedness and polarization of diverse beliefs across social issues. Our findings show that positions within this belief space predict new beliefs of individuals and estimate cognitive dissonance on the basis of the distance between existing and new beliefs. This study demonstrates how large language models, combined with collective online records of human beliefs, can offer insights into the fundamental principles that govern human belief formation. Lee et al. fine-tune large language models on debate data to create belief embeddings that capture the nuanced relationships between a wide range of beliefs, thus offering insight into how people form new beliefs.

External IDs:doi:10.1038/s41562-025-02228-z