Echoes of Power: Investigating Geopolitical Bias in US and China Large Language Models
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Abstract (arXiv)
Large Language Models (LLMs) have emerged as powerful tools for generating human-like text, transforming human-machine interactions. However, their widespread adoption has raised concerns about their potential to influence public opinion and shape political narratives. In this work, we investigate the geopolitical biases in US and Chinese LLMs, focusing on how these models respond to questions related to geopolitics and international relations. We collected responses from ChatGPT and DeepSeek to a set of geopolitical questions and evaluated their outputs through both qualitative and quantitative analyses. Our findings show notable biases in both models, reflecting distinct ideological perspectives and cultural influences. However, despite these biases, for a set of questions, the models' responses are more aligned than expected, indicating that they can address sensitive topics without necessarily presenting directly opposing viewpoints. This study highlights the potential of LLMs to shape public discourse and underscores the importance of critically assessing AI-generated content, particularly in politically sensitive contexts.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 19 of its 48 references that are in the map. First posted on arXiv 20 Mar 2025.
Builds on
19 of 48 references.
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7 more
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Citing papers per year: 2025: 2; 2026: 3.
5 papers.
- 2025Mapping Geopolitical Bias in 11 Large Language Models: A Bilingual, Dual-Framing Analysis of U.S.-China Tensionscited by 6
- 2025The Democratic Paradox in Large Language Models' Underestimation of Press Freedomcited by 0
- 2026Auditing demographic bias in AI-based emergency police dispatch: a cross-lingual evaluation of eleven large language modelscited by 0
- 2026The Shibboleth Effect: Auditing the Cross-Lingual Distributional Skew of Large Language Modelscited by 0
- 2026REGARD: Regional Affective Differences in Large Language Modelscited by 0
- 2026REGARD: Regional Affective Differences in Large Language Models22 Jul 2026
- 2026The Shibboleth Effect: Auditing the Cross-Lingual Distributional Skew of Large Language Models9 Jun 2026
- 2026Auditing demographic bias in AI-based emergency police dispatch: a cross-lingual evaluation of eleven large language models2 May 2026
- 2025The Democratic Paradox in Large Language Models' Underestimation of Press Freedom22 Jun 2025
- 2025Mapping Geopolitical Bias in 11 Large Language Models: A Bilingual, Dual-Framing Analysis of U.S.-China Tensions31 Mar 2025