@inproceedings{dussolle-etal-2025-ifeval,
    title = "{M}-{IFE}val: Multilingual Instruction-Following Evaluation",
    author = "Dussolle, Antoine  and
      Carde{\~n}a D{\'i}az, Andrea  and
      Sato, Shota  and
      Devine, Peter",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/dashboard/2025.findings-naacl.344/",
    doi = "10.18653/v1/2025.findings-naacl.344",
    pages = "6161--6176",
    ISBN = "979-8-89176-195-7",
    abstract = "Instruction following is a core capability of modern Large language models (LLMs), making evaluating this capability essential to understanding these models. The Instruction Following Evaluation (IFEval) benchmark from the literature does this using objective criteria, offering a measure of LLM performance without subjective AI or human judgement. However, it only includes English instructions, limiting its ability to assess LLMs in other languages.We propose the Multilingual Instruction Following Evaluation (M-IFEval) benchmark, expanding the evaluation to French, Japanese, and Spanish, with both general and language-specific instructions. Applying this benchmark to 8 state-of-the-art LLMs, we find that benchmark performance across languages and instruction types can vary widely, underscoring the importance of a multilingual benchmark for evaluating LLMs in a diverse cultural context."
}