Geopolitical alignment: Endorsement effects in large language models
Large language models (LLMs) are increasingly used to summarize and evaluate policy-relevant information, but it remains unclear whether their judgments are implicitly shaped by geopolitical cues. I study this question with an endorsement experiment in which four LLMs evaluate the same international economic and security policies after each policy is randomly described as supported by the United States, the European Union, China, or Russia. In the numeric-only condition, GPT-5, Claude Sonnet, and Gemini rate China- and Russia-endorsed policies substantially lower than identical policies endorsed by the United States or the European Union; DeepSeek is the main exception. A second condition...
| Authors | Maxim Chupilkin |
|---|---|
| Primary category | cs.CY |
| Published | 10 July 2026 |
| Last updated at source | 10 July 2026 |
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