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

arXiv: https://arxiv.org/abs/2503.16679 · HTML: https://mlsanity.com/paper/2503.16679/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 48 references that are in the map.
- First posted on arXiv 20 Mar 2025.

## Builds on (most cited first, 19 of 19)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2204.05862/) (2022)
- [Bias and Fairness in Large Language Models: A Survey](https://mlsanity.com/paper/2309.00770/) (2023)
- [Fine-Grained Human Feedback Gives Better Rewards for Language Model Training](https://mlsanity.com/paper/2306.01693/) (2023)
- [Cultural Bias and Cultural Alignment of Large Language Models](https://mlsanity.com/paper/2311.14096/) (2023)
- [The science of fake news](https://mlsanity.com/paper/2307.07903/) (2023)
- [AI model GPT-3 (dis)informs us better than humans](https://mlsanity.com/paper/2301.11924/) (2023)
- [Fake News in Sheep's Clothing: Robust Fake News Detection Against LLM-Empowered Style Attacks](https://mlsanity.com/paper/2310.10830/) (2023)
- [This Land is {Your, My} Land: Evaluating Geopolitical Biases in Language Models](https://mlsanity.com/paper/2305.14610/) (2023)
- [Political-LLM: Large Language Models in Political Science](https://mlsanity.com/paper/2412.06864/) (2024)
- [An Investigation of Large Language Models for Real-World Hate Speech Detection](https://mlsanity.com/paper/2401.03346/) (2024)
- [Fairness and Bias in Multimodal AI: A Survey](https://mlsanity.com/paper/2406.19097/) (2024)
- [FakeGPT: Fake News Generation, Explanation and Detection of Large Language Models](https://mlsanity.com/paper/2310.05046/) (2023)
- [HateBench: Benchmarking Hate Speech Detectors on LLM-Generated Content and Hate Campaigns](https://mlsanity.com/paper/2501.16750/) (2025)
- [Quite Good, but Not Enough: Nationality Bias in Large Language Models -- A Case Study of ChatGPT](https://mlsanity.com/paper/2405.06996/) (2024)
- [LIBRA: Measuring Bias of Large Language Model from a Local Context](https://mlsanity.com/paper/2502.01679/) (2025)

## Built on by (most cited first, 5 of 5)

- [Mapping Geopolitical Bias in 11 Large Language Models: A Bilingual, Dual-Framing Analysis of U.S.-China Tensions](https://mlsanity.com/paper/2503.23688/) (2025)
- [The Democratic Paradox in Large Language Models' Underestimation of Press Freedom](https://mlsanity.com/paper/2506.18045/) (2025)
- [Auditing demographic bias in AI-based emergency police dispatch: a cross-lingual evaluation of eleven large language models](https://mlsanity.com/paper/2605.01451/) (2026)
- [The Shibboleth Effect: Auditing the Cross-Lingual Distributional Skew of Large Language Models](https://mlsanity.com/paper/2606.11082/) (2026)
- [REGARD: Regional Affective Differences in Large Language Models](https://mlsanity.com/paper/2607.20722/) (2026)

## 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.