mlsanityData to 14 Oct 2026

Mapping Geopolitical Bias in 11 Large Language Models: A Bilingual, Dual-Framing Analysis of U.S.-China Tensions

arXiv 2503.23688 ↗First posted 31 Mar 2025cs.CL cs.HCcited by 7

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Abstract (arXiv)

Large language models are how hundreds of millions of people now encounter contested political questions, raising a subtle measurement problem: a model that simply agrees with whatever it is told can masquerade as biased, contaminating any claim that models hold political opinions. We address this by importing balanced keying from survey psychometrics, posing each proposition and its swapped reverse and signing the response so acquiescence cancels and genuine conviction accumulates. The result is a reproducible, quantitative instrument that maps geopolitical stance across 11 models and 2 languages (19,712 responses). Developer origin, query language and issue domain emerge as three near-equal, additive factors; every model, including those built in the United States, leans more Pro-China in Mandarin; and two models with identical agreement bias are told apart, one neutral, one biased. We release it as an open, interactive tool that extends to any contested-opinion domain.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 23 of its 39 references that are in the map. First posted on arXiv 31 Mar 2025.

Builds on

23 of 39 references.

References · 23

  1. 2022Training language models to follow instructions with human feedbackcited by 15,971
  2. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  3. 2022Constitutional AI: Harmlessness from AI Feedbackcited by 3,050
  4. 2023Towards Understanding Sycophancy in Language Modelscited by 1,040
  5. 2023ChatGPT Outperforms Crowd-Workers for Text-Annotation Taskscited by 870
  6. 2022Discovering Language Model Behaviors with Model-Written Evaluationscited by 836
  7. 2023Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formattingcited by 817
  8. 2023Whose Opinions Do Language Models Reflect?cited by 773
  9. 2023Bias and Fairness in Large Language Models: A Surveycited by 639
  10. 2023Towards Measuring the Representation of Subjective Global Opinions in Language Modelscited by 437
  11. 2023Large Language Models Sensitivity to The Order of Options in Multiple-Choice Questionscited by 263
  12. 2023From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Modelscited by 260
11 more
  1. 2023The political ideology of conversational AI: Converging evidence on ChatGPT's pro-environmental, left-libertarian orientationcited by 189
  2. 2024Political Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language Modelscited by 162
  3. 2023Having Beer after Prayer? Measuring Cultural Bias in Large Language Modelscited by 153
  4. 2023Cultural Bias and Cultural Alignment of Large Language Modelscited by 149
  5. 2024The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Modelscited by 144
  6. 2024On the Conversational Persuasiveness of Large Language Models: A Randomized Controlled Trialcited by 109
  7. 2024The Political Preferences of LLMscited by 84
  8. 2024Bias in Large Language Models: Origin, Evaluation, and Mitigationcited by 77
  9. 2024Large Language Models Reflect the Ideology of their Creatorscited by 57
  10. 2025Human Decision-making is Susceptible to AI-driven Manipulationcited by 19
  11. 2025Echoes of Power: Investigating Geopolitical Bias in US and China Large Language Modelscited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 6.

7 papers.

  1. 2026LLMs as Strategic Actors: Behavioral Alignment, Risk Calibration, and Argumentation Framing in Geopolitical Simulationscited by 4
  2. 2025Decoding Alignment: A Critical Survey of LLM Development Initiatives through Value-setting and Data-centric Lenscited by 3
  3. 2026BiasLab: A Multilingual Dual-Framing Framework for LLM Bias Measurement, Applied to Workplace and HR Contextscited by 0
  4. 2026Which Institutional Frameworks Do Chatbots Assume? Auditing Jurisdictional Defaults in Multilingual LLMscited by 0
  5. 2026The Shibboleth Effect: Auditing the Cross-Lingual Distributional Skew of Large Language Modelscited by 0
  6. 2026Poli-Bias: Understanding and Measuring Large Language Model Biases in International Political Conflictscited by 0
  7. 2026Templated or fully synthetic? Prompt construction as a confound in measuring LLM political stance beyond writing assistancecited by 0