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

2025/06/07 by Mikhail Salnikov, Dmitrii Korzh, Salnikov, Mikhail +17 · 3 citations
Computer Science · Medicine · Social Sciences · #Artificial Intelligence in Healthcare and Education #Computation and Language (cs.CL) #Computational and Text Analysis Methods #FOS: Computer and information sciences #Topic Modeling

paper · pdf · doi:10.48550/arxiv.2506.06751

openalex publication_date 2025/06/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

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• Estimating the Geopolitical Preferences of Large Language Models from United Nations Voting Data
• Decoding Alignment: A Critical Survey of LLM Development Initiatives through Value-setting and Data-centric Lens
• HAMSA: Hijacking Aligned Compact Models via Stealthy Automation

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