当你的问题不是英语时不要信任 ChatGPT：关于 LLMs 多语言能力与类型的研究
Don't Trust ChatGPT when Your Question is not in English: A Study of Multilingual Abilities and Types of LLMs
AI总结：
本文研究了LLMs在多语言环境下的性能差异与跨语言泛化现象，提出基于反向翻译的提示方法，发现GPT在多语言场景下表现出高度类似翻译的行为。
摘要：
大型语言模型（LLMs）展现出了卓越的自然语言理解能力，并在近年来在各种自然语言处理（NLP）任务中表现出色。尽管大多数 LLMs 主要使用英语进行训练，但多项研究证明了它们在许多其他语言中具有相当的性能。然而，关于 LLMs 如何获得其多语言能力以及性能如何在不同语言之间变化的基本问题仍然存在。这些探究对于 LLMs 的研究至关重要，因为用户和研究人员通常来自不同的语言背景，这可能会影响他们对 LLMs 结果的使用和解释。在这项工作中，我们提出了一种系统的方法来量化多语言环境下 LLMs 的性能差异。我们研究了 LLMs 中的跨语言泛化现象，即不充足的多语言训练数据反而导致了高级的多语言能力。为此，我们采用了一种新颖的基于反向翻译的提示方法。结果表明，GPT 在多语言环境下表现出高度类似翻译的行为。
英文摘要：
Large Language Models (LLMs) have demonstrated exceptional natural language understanding abilities and have excelled in a variety of natural language processing (NLP)tasks in recent years. Despite the fact that most LLMs are trained predominantly in English, multiple studies have demonstrated their comparative performance in many other languages. However, fundamental questions persist regarding how LLMs acquire their multi-lingual abilities and how performance varies across different languages. These inquiries are crucial for the study of LLMs since users and researchers often come from diverse language backgrounds, potentially influencing their utilization and interpretation of LLMs' results. In this work, we propose a systematic way of qualifying the performance disparities of LLMs under multilingual settings. We investigate the phenomenon of across-language generalizations in LLMs, wherein insufficient multi-lingual training data leads to advanced multi-lingual capabilities. To accomplish this, we employ a novel back-translation-based prompting method. The results show that GPT exhibits highly translating-like behaviour in multilingual settings.
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。
