跨语言科学：评估大语言模型对科学论文的多语言翻译能力
Science Across Languages: Assessing LLM Multilingual Translation of Scientific Papers
AI总结：
本研究针对英文期刊给非英语母语研究者造成的障碍，提出保留JATS XML格式的LLM科学论文多语言翻译方法，通过新型QA基准和用户研究验证了高准确性，并展示了用上下文学习缓解过度翻译的可行性。
摘要：
科学研究本质上具有全球性。然而，绝大多数学术期刊仅以英文出版，这给非英语母语的研究人员造成了障碍。本研究利用大语言模型（LLM）翻译已发表的科学文章，同时保留其原生JATS XML格式，从而开发出一种可供学术期刊落地使用的实用自动化方法。借助该方法，我们将多个科学学科的文章翻译成28种语言。为评估翻译准确性，我们提出了一种新颖的问答（QA）基准测试方法：由LLM根据原文生成基于理解的问题，再依据译文回答这些问题。我们的基准测试结果显示平均表现达95.9%，表明关键科学细节得到了准确传达。在一项用户研究中，我们将15位研究人员的科学论文翻译成他们的母语，发现这些作者一致认为译文准确捕捉了原文的信息。有趣的是，三分之一的作者发现许多技术术语被“过度翻译”，他们更倾向于保留更熟悉的英文术语不译。最后，我们展示了如何利用上下文学习技术使翻译符合领域特定偏好（例如缓解过度翻译问题），凸显了LLM驱动的科学翻译的适应性与实用性。代码与翻译后的文章可在https://hankleid.github.io/ProjectMundo获取。
英文摘要：
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, in which an LLM generates comprehension-based questions from the original text and then answers them based on the translated text. Our benchmark results show an average performance of 95.9%, showing that the key scientific details are accurately conveyed. In a user study, we translate the scientific papers of 15 researchers into their native languages, finding that the authors consistently found the translations to accurately capture the original information in their articles. 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. The code and translated articles are available at https://hankleid.github.io/ProjectMundo.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
