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        <title>The Language You Ask In: Language-Conditioned Ideological Divergence in LLM Analysis of Contested Political Documents</title>
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    <abstract>Large language models are increasingly used to interpret politically contested questions, value-laden material on which there is no single correct answer, only competing interpretive traditions. We ask whether a model’s choice among those traditions can turn on the language of the prompt rather than the content. Comparing two frontier models, ChatGPT 5.2 and Claude Opus 4.5, on one contested Ukrainian civil-society document under semantically matched Russian and Ukrainian prompts, we find that both shift along the same axis on identical source text: Russian prompts elicit delegitimizing readings of the document’s authors and Ukrainian prompts legitimating ones. The magnitude is model-dependent but neither model is neutral: each adopts a language-dependent stance, and the difference is one of degree. Because contested political questions admit no correct reading against which to measure, we read this as language-conditioned variation in which interpretive tradition a model activates: the model neither holds a single stance nor surfaces the plurality of available ones, but silently adopts the dominant frame of the prompt’s language. We draw out the consequences for pluralism-aware evaluation, which must probe the same content across the languages a model serves, and for pluralistic alignment in multilingual settings.</abstract>
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%0 Conference Proceedings
%T The Language You Ask In: Language-Conditioned Ideological Divergence in LLM Analysis of Contested Political Documents
%A Smirnov, Oleg
%Y Xiong, Deyi
%Y Branco, António
%Y Zhang, Hongming
%Y Dong, Yue
%Y Wang, Benyou
%Y Zhang, Wenxuan
%Y Zhou, Li
%Y Zheng, Jingting
%S Proceedings of the First Workshop on Pluralistic Value Alignment of LLMs
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-453-8
%F smirnov-2026-language
%X Large language models are increasingly used to interpret politically contested questions, value-laden material on which there is no single correct answer, only competing interpretive traditions. We ask whether a model’s choice among those traditions can turn on the language of the prompt rather than the content. Comparing two frontier models, ChatGPT 5.2 and Claude Opus 4.5, on one contested Ukrainian civil-society document under semantically matched Russian and Ukrainian prompts, we find that both shift along the same axis on identical source text: Russian prompts elicit delegitimizing readings of the document’s authors and Ukrainian prompts legitimating ones. The magnitude is model-dependent but neither model is neutral: each adopts a language-dependent stance, and the difference is one of degree. Because contested political questions admit no correct reading against which to measure, we read this as language-conditioned variation in which interpretive tradition a model activates: the model neither holds a single stance nor surfaces the plurality of available ones, but silently adopts the dominant frame of the prompt’s language. We draw out the consequences for pluralism-aware evaluation, which must probe the same content across the languages a model serves, and for pluralistic alignment in multilingual settings.
%U https://aclanthology.org/2026.plurvallm-1.9/
%P 104-142

Markdown (Informal)

[The Language You Ask In: Language-Conditioned Ideological Divergence in LLM Analysis of Contested Political Documents](https://aclanthology.org/2026.plurvallm-1.9/) (Smirnov, PlurVA-LLM 2026)

• The Language You Ask In: Language-Conditioned Ideological Divergence in LLM Analysis of Contested Political Documents (Smirnov, PlurVA-LLM 2026)

ACL

• Oleg Smirnov. 2026. The Language You Ask In: Language-Conditioned Ideological Divergence in LLM Analysis of Contested Political Documents. In Proceedings of the First Workshop on Pluralistic Value Alignment of LLMs, pages 104–142, Hengqin, China. Association for Computational Linguistics.