Large language models (LLMs) with Chain-of-thought (CoT) have recently emerged as a powerful technique for eliciting reasoning to improve various downstream tasks. As most research mainly focuses on English, with few explorations in a multilingual context, the question of how reliable this reasoning capability is in different languages is still open. To address it directly, we study multilingual reasoning consistency across multiple languages, using popular open-source LLMs. First, we compile the first large-scale multilingual math reasoning dataset, mCoT-MATH, covering eleven diverse languages. Then, we introduce multilingual CoT instruction tuning to boost reasoning capability across languages, thereby improving model consistency. While existing LLMs show substantial variation across the languages we consider, and especially low performance for lesser resourced languages, our 7B parameter model mCoT achieves impressive consistency across languages, and superior or comparable performance to close- and open-source models even of much larger sizes.

翻译：基于思维链（CoT）的大语言模型（LLMs）已成为通过激发推理能力以提升各类下游任务性能的重要技术。由于现有研究主要集中于英语，在多语言语境下的探索较少，这种推理能力在不同语言中的可靠性仍待探究。为此，我们基于主流开源LLMs系统研究了跨多语言的推理一致性问题。首先，我们构建了首个大规模多语言数学推理数据集mCoT-MATH，涵盖十一种不同语言。随后，我们提出多语言CoT指令调优方法，以增强模型跨语言的推理能力，从而提升其一致性。实验表明，现有LLMs在所考察语言间存在显著性能差异，尤其在资源稀缺语言上表现欠佳；而我们的70亿参数模型mCoT在跨语言一致性方面表现优异，其性能与规模远超自身的闭源及开源模型相当甚至更优。