@inproceedings{kleidermacher-zou-2026-science,
    title = "Science Across Languages: Assessing {LLM} Multilingual Translation of Scientific Papers",
    author = "Kleidermacher, Hannah Calzi  and
      Zou, James",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.204/",
    pages = "3932--3947",
    ISBN = "979-8-89176-386-9",
    abstract = "Scientific research is inherently global. However, the vast majority of academic journals are published exclusively in English, creating barriers for non-native-English-speaking researchers. In this study, we leverage large language models (LLMs) to translate published scientific articles while preserving their native JATS XML formatting, thereby developing a practical, automated approach for implementation by academic journals. Using our approach, we translate articles across multiple scientific disciplines into 28 languages. To evaluate translation accuracy, we introduce a novel question-and-answer (QA) benchmarking method and show an average performance of 95.9{\%}, indicating that the key scientific details are accurately conveyed. In a user study, we translate the scientific papers of 15 researchers into their native languages. Interestingly, a third of the authors found many technical terms ``overtranslated,'' expressing a preference to keep terminology more familiar in English untranslated. Finally, we demonstrate how in-context learning techniques can be used to align translations with domain-specific preferences such as mitigating overtranslation, highlighting the adaptability and utility of LLM-driven scientific translation."
}