这篇论文用实验数据告诉你，大模型评政策时会看谁站台——同一政策挂美国旗得分高，挂中国旗得分低。GPT-5、Claude Sonnet、Gemini、DeepSeek表现各异，值得关注。
一项论文使用背书实验测试GPT-5、Claude Sonnet、Gemini和DeepSeek四款大模型对国际经济与安全政策的评分。当政策被描述为获得美国或欧盟支持时，模型评分显著高于被描述为获得中国或俄罗斯支持的政策，数值条件中GPT-5、Claude Sonnet和Gemini对中国和俄罗斯背书的政策评分分别降低约10%-20%。加入要求模型提供理由后，GPT-5和Claude Sonnet的西方/非西方差距保持不变，Gemini的惩罚减弱，但DeepSeek此前无差距转而呈现对中国和俄罗斯的惩罚。模型理由显示西方背书被视为可信度信号，而中俄背书与数据安全、主权、监控或地缘政治风险关联。
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 asks models to provide a short justification with the score. This request leaves the broad Western/non-Western gap intact for GPT-5 and Claude Sonnet, attenuates Gemini's penalties, and sharply activates China and Russia penalties in DeepSeek. The justifications indicate that Western endorsement is often treated as a credibility cue, whereas Chinese and Russian endorsement is treated as a cue for data security, sovereignty, surveillance, or geopolitical risk. These findings show that LLM policy evaluations can depend on the identity of a foreign endorser even when policy content is held fixed.