mlsanityData to 7 Oct 2026

Echoes of Power: Investigating Geopolitical Bias in US and China Large Language Models

arXiv 2503.16679 ↗First posted 20 Mar 2025cs.CY cs.AI cs.HCcited by 5

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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.

References · 19

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
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  12. 2023This Land is {Your, My} Land: Evaluating Geopolitical Biases in Language Modelscited by 31
7 more
  1. 2024Political-LLM: Large Language Models in Political Sciencecited by 27
  2. 2024An Investigation of Large Language Models for Real-World Hate Speech Detectioncited by 20
  3. 2024Fairness and Bias in Multimodal AI: A Surveycited by 19
  4. 2023FakeGPT: Fake News Generation, Explanation and Detection of Large Language Modelscited by 16
  5. 2024Quite Good, but Not Enough: Nationality Bias in Large Language Models -- A Case Study of ChatGPTcited by 13
  6. 2025HateBench: Benchmarking Hate Speech Detectors on LLM-Generated Content and Hate Campaignscited by 10
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.