用联合国投票数据测了四个大模型的政治立场，发现DeepSeek最亲法，GPT-5最亲俄，跟开发者国家不一样。
论文使用动态序数理想点方法，将LLM作为受访者分析1946-2025年5555份联合国决议。支持率从DeepSeek的37.8%到GPT-5的97.3%。在21世纪，GPT-5、Claude Sonnet和Gemini最接近俄罗斯，DeepSeek最接近法国，所有四个模型均远离美国。针对美国反对而中俄支持的2104项决议，GPT-5支持96.1%，Gemini 83.4%，Claude Sonnet 65.2%，DeepSeek 36.1%。
Estimating the Geopolitical Preferences of Large Language Models from United Nations Voting Data
How should researchers measure the geopolitical preferences expressed by large language models (LLMs)? Existing audits commonly rely on surveys and simple tests, but international-relations research has long recognized that measuring geopolitical preferences is difficult and has developed methods for recovering them from observed choices. This paper applies a dynamic ordinal ideal-point approach from international relations, treating LLMs as respondents to the full texts of 5,555 divisive, recorded, adopted resolutions considered in regular sessions of the UN General Assembly from 1946 through 2025. Support ranges from 37.8% for DeepSeek to 97.3% for GPT-5. Surprisingly, in the twenty-first century, GPT-5, Claude Sonnet, and Gemini are closest among the permanent five to Russia; DeepSeek is closest to France; and all four are farthest from the United States. Among 2,104 resolutions opposed by the United States but supported by China and Russia/USSR, GPT-5 supported 96.1%, Gemini 83.4%, Claude Sonnet 65.2%, and DeepSeek 36.1%. The findings show that a model's expressed geopolitical position can differ markedly from that of its developer's home country, especially in international politics, where state actions can diverge from the stated principles prevalent in the texts on which models are trained.