翻译是否就是一切？关于用大型语言模型解决多语言任务的研究
Is Translation All You Need? A Study on Solving Multilingual Tasks with Large Language Models
AI总结：
本研究探讨翻译对多语言任务的影响，发现翻译成英语虽能提升英语中心LLMs的NLP性能，但对文化相关任务，母语提示更有效，呼吁超越英语中心的多语言评估与开发。
摘要：
大型语言模型（LLMs）已展现出多语言能力，然而由于训练语料库的不平衡，它们大多以英语为中心。虽然先前的研究利用这一偏差通过翻译来提升多语言性能，但这些研究在很大程度上仅限于自然语言处理（NLP）任务。在本工作中，我们将评估扩展到真实世界的用户查询和非英语中心的大型语言模型，从而对多语言性能进行更广泛的考察。我们的关键贡献在于证明：虽然将内容翻译成英语可以提升英语中心LLMs在NLP任务上的性能，但这并非普遍最优。对于需要深度语言理解的文化相关任务，使用母语提示更为有效，因为它能更好地捕捉文化和语言的细微差别。我们的实验揭示了多语言背景下不同LLMs和任务之间的多样化行为，强调了需要一种更全面的多语言评估方法的必要性。因此，我们呼吁加大力度开发和评估超越英语中心范式的大型语言模型。
英文摘要：
Large language models (LLMs) have demonstrated multilingual capabilities, yet they are mostly English-centric due to the imbalanced training corpora. While prior works have leveraged this bias to enhance multilingual performance through translation, they have been largely limited to natural language processing (NLP) tasks. In this work, we extend the evaluation to real-world user queries and non-English-centric LLMs, offering a broader examination of multilingual performance. Our key contribution lies in demonstrating that while translation into English can boost the performance of English-centric LLMs on NLP tasks, it is not universally optimal. For culture-related tasks that need deep language understanding, prompting in the native language proves more effective as it better captures the nuances of culture and language. Our experiments expose varied behaviors across LLMs and tasks in the multilingual context, underscoring the need for a more comprehensive approach to multilingual evaluation. Therefore, we call for greater efforts in developing and evaluating LLMs that go beyond English-centric paradigms.
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。
