@inproceedings{labat-etal-2026-polyglots,
    title = "Polyglots or Multitudes? Multilingual {LLM} Answers to Value-laden Multiple-Choice Questions",
    author = "Labat, L{\'e}o  and
      Ollion, Etienne  and
      Yvon, Fran{\c{c}}ois",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.156/",
    pages = "3382--3398",
    ISBN = "979-8-89176-380-7",
    abstract = "Multiple-Choice Questions (MCQs) are often used to assess knowledge, reasoning abilities, and even values encoded in large language models (LLMs). While the effect of multilingualism has been studied on LLM factual recall, this paper seeks to investigate the less explored question of language-induced variation in value-laden MCQ responses. Are multilingual LLMs consistent in their responses across languages, *i.e.* behave like theoretical *polyglots*, or do they answer value-laden MCQs depending on the language of the question, like a *multitude* of monolingual models expressing different values through a single model? We release a new corpus, the Multilingual European Value Survey (**MEVS**), which, unlike prior work relying on machine translation or ad hoc prompts, solely comprises human-translated survey questions aligned in 8 European languages. We administer a subset of those questions to over thirty multilingual LLMs of various sizes, manufacturers and alignment-fine-tuning status under comprehensive, controlled prompt variations including answer order, symbol type, and tail character. Our results show that while larger, instruction-tuned models display higher overall consistency, the robustness of their responses varies greatly across questions, with certain MCQs eliciting total agreement *within and across* models while others leave LLM answers split. Language-specific behavior seems to arise in all consistent, instruction-fine-tuned models, but only on certain questions, warranting a further study of the selective effect of preference fine-tuning."
}