Democratic or Authoritarian? Probing a New Dimension of Political Biases in Large Language Models

TL;DR

This paper introduces a new methodology to evaluate large language models' alignment with democracy or authoritarianism, revealing biases that vary with language and highlighting the importance of broader geopolitical bias assessment.

Contribution

It proposes novel metrics and probing techniques to assess LLMs' political biases along the democracy-authoritarian spectrum, expanding beyond traditional socio-political axes.

Findings

LLMs generally favor democratic values and leaders.

Bias toward authoritarian figures increases when prompted in Mandarin.

Models often cite authoritarian figures as role models, even outside political contexts.

Abstract

As Large Language Models (LLMs) become increasingly integrated into everyday life and information ecosystems, concerns about their implicit biases continue to persist. While prior work has primarily examined socio-demographic and left--right political dimensions, little attention has been paid to how LLMs align with broader geopolitical value systems, particularly the democracy--authoritarianism spectrum. In this paper, we propose a novel methodology to assess such alignment, combining (1) the F-scale, a psychometric tool for measuring authoritarian tendencies, (2) FavScore, a newly introduced metric for evaluating model favorability toward world leaders, and (3) role-model probing to assess which figures are cited as general role-models by LLMs. We find that LLMs generally favor democratic values and leaders, but exhibit increased favorability toward authoritarian figures when prompted in Mandarin. Further, models are found to often cite authoritarian figures as role models, even outside explicit political contexts. These results shed light on ways LLMs may reflect and potentially reinforce global political ideologies, highlighting the importance of evaluating bias beyond conventional socio-political axes. Our code is available at: https://github.com/irenestrauss/Democratic-Authoritarian-Bias-LLMs.