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  3. A Semantic Embedding Space Based On Large Language Models For Modelling Human Beliefs.
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  3. A Semantic Embedding Space Based On Large Language Models For Modelling Human Beliefs.

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

Byunghwee Lee1, Rachith Aiyappa1, Yong-Yeol Ahn1

  • 1Center for Complex Networks and Systems Research, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA.

Nature Human Behaviour
|June 4, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Researchers mapped thousands of beliefs using large language models and online debate data. This reveals how beliefs interconnect, predict new beliefs, and estimate cognitive dissonance, offering insights into belief formation.

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Area of Science:

  • Cognitive Science
  • Computational Social Science
  • Artificial Intelligence

Background:

  • Beliefs are fundamental to human cognition, decision-making, and social interactions.
  • Understanding belief interrelationships is key, but prior research is limited in scope and methodology.
  • Existing studies often focus on specific issues and rely on traditional survey methods.

Purpose of the Study:

  • To develop a novel method for studying the interplay of thousands of beliefs.
  • To leverage online user debate data and large language models for belief analysis.
  • To map beliefs into a neural embedding space for nuanced investigation.

Main Methods:

  • Utilized a fine-tuned large language model to construct a neural embedding space for beliefs.
  • Analyzed extensive online user debate data to capture belief interrelationships.
  • Mapped diverse beliefs onto the constructed embedding space to represent their connections and polarization.

    • Main Results:

    • The belief space effectively captures the interconnectedness and polarization of beliefs across various social issues.
    • Positions within the belief space can predict individuals' new beliefs.
    • The model can estimate cognitive dissonance based on the spatial distance between existing and new beliefs.

    Conclusions:

    • Large language models combined with collective online belief data offer powerful insights into belief formation.
    • This approach provides a scalable and nuanced method for studying complex belief systems.
    • The findings contribute to understanding fundamental principles governing human belief structures and dynamics.