Large Language Models as Implicit Sociological Models: Reconstructing Voting Behaviour from Sociodemographic Profiles

Research from arXiv suggests that Large Language Models (LLMs), by virtue of their training on vast internet data, implicitly encode extensive statistical regularities concerning social identities, attitudes, and political behavior. A new methodological framework proposes leveraging these LLMs as 'implicit sociological models' to reconstruct aggregate voting behavior from individual-level sociodemographic profiles. Validated against the 2021 Czech parliamentary election, current LLMs demonstrated the ability to reproduce official election outcomes with low mean absolute error, indicating their potential for modeling complex social phenomena.

• LLMs trained on internet corpora capture statistical regularities related to social identities, attitudes, and political behaviors.
• A new framework uses LLMs as implicit sociological models, conditioning them on demographic profiles to predict probabilistic turnout and party preferences.
• Individual LLM outputs are aggregated through a soft voting procedure to reconstruct overall voting behavior.
• The method was validated against the 2021 Czech parliamentary election, showing LLMs can reproduce official outcomes with low mean absolute error.

Reported by arXiv — Computers and Society · International. This briefing summarises the publication for executive use; the document itself is not reproduced here.