A semantic embedding space based on large language models for modelling human beliefs
This work addresses the challenge of understanding belief formation and polarization for social science and AI, though it is incremental as it builds on existing LLM and embedding methods.
The researchers tackled the problem of modeling the interplay between thousands of human beliefs by leveraging online debate data and a fine-tuned large language model to create a neural embedding space. Their results showed that positions in this space predict new beliefs and estimate cognitive dissonance based on distances between beliefs.
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 relied heavily on surveys. We propose a method to study the nuanced interplay between thousands of beliefs by leveraging an online user debate data and mapping beliefs onto a neural embedding space constructed using a fine-tuned large language model (LLM). 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 based on the distance between existing and new beliefs. This study demonstrates how LLMs, combined with collective online records of human beliefs, can offer insights into the fundamental principles that govern human belief formation.