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

arXiv: https://arxiv.org/abs/2506.12758 · HTML: https://mlsanity.com/paper/2506.12758/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2025.

## Built on by (most cited first, 4 of 4)

- [Social Welfare Function Leaderboard: When LLM Agents Allocate Social Welfare](https://mlsanity.com/paper/2510.01164/) (2025)
- [Prioritize Economy or Climate Action? Investigating ChatGPT Response Differences Based on Inferred Political Orientation](https://mlsanity.com/paper/2511.04706/) (2025)
- [When Do Language Models Endorse Limitations on Human Rights Principles?](https://mlsanity.com/paper/2603.04217/) (2026)
- [AuAu: A Benchmark for Auditing Authoritarian Alignment in Large Language Models](https://mlsanity.com/paper/2606.16127/) (2026)

## Abstract (arXiv)

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.