# Geopolitical biases in LLMs: what are the "good" and the "bad" countries according to contemporary language models

arXiv: https://arxiv.org/abs/2506.06751 · HTML: https://mlsanity.com/paper/2506.06751/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 7 Jun 2025.

## Builds on (most cited first, 9 of 9)

- [Bias and Fairness in Large Language Models: A Survey](https://mlsanity.com/paper/2309.00770/) (2023)
- [Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Study](https://mlsanity.com/paper/2303.17466/) (2023)
- [Political Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language Models](https://mlsanity.com/paper/2402.16786/) (2024)
- [Cross-Lingual Consistency of Factual Knowledge in Multilingual Language Models](https://mlsanity.com/paper/2310.10378/) (2023)
- [Hidden Persuaders: LLMs' Political Leaning and Their Influence on Voters](https://mlsanity.com/paper/2410.24190/) (2024)
- [Investigating Bias in LLM-Based Bias Detection: Disparities between LLMs and Human Perception](https://mlsanity.com/paper/2403.14896/) (2024)
- [This Land is {Your, My} Land: Evaluating Geopolitical Biases in Language Models](https://mlsanity.com/paper/2305.14610/) (2023)
- [On the Relationship between Truth and Political Bias in Language Models](https://mlsanity.com/paper/2409.05283/) (2024)
- [Language Models Get a Gender Makeover: Mitigating Gender Bias with Few-Shot Data Interventions](https://mlsanity.com/paper/2306.04597/) (2023)

## Built on by (most cited first, 6 of 6)

- [Decoding Alignment: A Critical Survey of LLM Development Initiatives through Value-setting and Data-centric Lens](https://mlsanity.com/paper/2508.16982/) (2025)
- [HAMSA: Hijacking Aligned Compact Models via Stealthy Automation](https://mlsanity.com/paper/2508.16484/) (2025)
- [Can AI Make Conflicts Worse? An Alignment Failure in LLM Deployment Across Conflict Contexts](https://mlsanity.com/paper/2605.22720/) (2026)
- [The Shibboleth Effect: Auditing the Cross-Lingual Distributional Skew of Large Language Models](https://mlsanity.com/paper/2606.11082/) (2026)
- [Unintended Effects of Geographic Conditioning in Large Language Models](https://mlsanity.com/paper/2606.18124/) (2026)
- [Templated or fully synthetic? Prompt construction as a confound in measuring LLM political stance beyond writing assistance](https://mlsanity.com/paper/2608.11008/) (2026)

## Abstract (arXiv)

This paper evaluates geopolitical biases in LLMs with respect to various countries though an analysis of their interpretation of historical events with conflicting national perspectives (USA, UK, USSR, and China). We introduce a novel dataset with neutral event descriptions and contrasting viewpoints from different countries. Our findings show significant geopolitical biases, with models favoring specific national narratives. Additionally, simple debiasing prompts had a limited effect in reducing these biases. Experiments with manipulated participant labels reveal models' sensitivity to attribution, sometimes amplifying biases or recognizing inconsistencies, especially with swapped labels. This work highlights national narrative biases in LLMs, challenges the effectiveness of simple debiasing methods, and offers a framework and dataset for future geopolitical bias research.