Chain-of-Thought (CoT) prompting empowers the reasoning abilities of Large Language Models (LLMs), eliciting them to solve complex reasoning tasks step-by-step. However, with the success of CoT methods, the ability to deliver multi-step reasoning remains limited to English due to the imbalance in the distribution of the pre-training data, making the other languages a barrier. In this work, we propose a Cross-lingual multi-step reasoning approach, aiming to align reasoning processes across different languages. In particular, our method, through a Self-consistent Cross-lingual prompting mechanism inspired by the Tree-of-Thoughts approach, delivers multi-step reasoning paths in different languages that, during the steps, lead to the final solution. Our experimental evaluations show that our method significantly outperforms existing prompting methods, reducing the number of interactions and achieving state-of-the-art performance.

翻译：链式思维提示能够激发大语言模型的推理能力，引导其逐步解决复杂推理任务。然而，尽管链式思维方法已取得显著成效，由于预训练数据分布的不平衡性，多步推理能力仍局限于英语，导致其他语言成为障碍。本研究提出一种跨语言多步推理方法，旨在对齐不同语言的推理过程。具体而言，我们的方法受思维树启发，通过自洽跨语言提示机制，在不同语言中生成多步推理路径，并逐步导向最终解决方案。实验评估表明，我们的方法显著优于现有提示方法，在减少交互次数的同时实现了最先进的性能。