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Geopolitical Divisions Across Languages in Large Language Models

A study of 67,200 responses from GPT, Claude and Gemini found that the language of a question changes how the models assess the war in Ukraine, with the balance of Russia-leaning and Ukraine-leaning answers varying across 112 languages. Grouping responses by countries' official languages produced a pattern resembling worldwide political divisions, where more Russia-leaning answers corresponded to more favourable public views of Russia, less support for Ukraine in United Nations votes, and less aid to Ukraine, according to the arXiv paper 2609.20005v1. The pattern recurred across all three models and held when individual statement pairs were removed, suggesting a possible route through which information warfare may shape AI training text and spread geopolitical biases.

by read1 min views1 publishedSep 18, 2026

arXiv:2609.20005v1 Announce Type: new Abstract: People increasingly turn to AI chatbots for news and explanations of world events. But do they receive the same political answers when they ask in different languages? Here we show that the language of a question can change how the same AI systems assess the war in Ukraine. We ask GPT, Claude and Gemini to evaluate twenty statements about the war in 112 languages, collecting 67,200 responses. The balance between Russia-leaning and Ukraine-leaning responses differs across languages. When we group responses by countries' official languages, they follow a pattern resembling worldwide political divisions: relatively more Russia-leaning answers correspond to more favourable public views of Russia, less support for Ukraine in United Nations votes, and less aid to Ukraine. The broad pattern recurs across all three models and remains when individual statement pairs are removed. Our findings suggest a possible route through which information warfare may shape the text used to train AI models, which may in turn spread geopolitical biases.

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