@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Science Across Languages: Assessing LLM Multilingual Translation of Scientific Papers
%A Kleidermacher, Hannah Calzi
%A Zou, James
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Findings of the Association for Computational Linguistics: EACL 2026
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-386-9
%F kleidermacher-zou-2026-science
%X 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.
%U https://aclanthology.org/2026.findings-eacl.204/
%P 3932-3947
Markdown (Informal)
[Science Across Languages: Assessing LLM Multilingual Translation of Scientific Papers](https://aclanthology.org/2026.findings-eacl.204/) (Kleidermacher & Zou, Findings 2026)
ACL