Mapping Geopolitical Bias in 11 Large Language Models: A Bilingual, Dual-Framing Analysis of U.S.-China Tensions
arXiv:2503.23688v2 Announce Type: replace Abstract: 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 m
The proliferation of LLMs into daily information consumption for millions makes their inherent biases a critical and immediate concern for public discourse and geopolitical relations.
Understanding and quantifying geopolitical bias in LLMs is crucial for assessing their reliability as information sources and their potential influence on public opinion and international relations.
The ability to systematically measure and potentially mitigate geopolitical bias in LLMs means a higher potential for more neutral information dissemination or, conversely, a more precise understanding of embedded biases.
- · Governments seeking to understand external influence
- · Academics researching AI ethics
- · Developers building less biased LLMs
- · Users seeking impartial information
- · LLMs with unaddressed geopolitical biases
- · Entities relying on unquantified LLM biases
- · Propaganda efforts via unexamined AI
Systematic measurement techniques for LLM bias are developed and adopted by researchers and developers.
AI developers implement new techniques to reduce or neutralize specific geopolitical biases in their models.
Nations begin to scrutinize and potentially regulate the geopolitical neutrality of generative AI deployed within their borders.
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