Reasoning methods, best exemplified by the well-known Chain-of-Thought (CoT), empower the reasoning abilities of Large Language Models (LLMs) by eliciting them to solve complex tasks in a step-by-step manner. Although they are achieving significant success, the ability to deliver multi-step reasoning remains limited to English because of the imbalance in the distribution of pre-training data, which makes other languages a barrier. In this paper, we propose Cross-lingual Tree-of-Thoughts (Cross-ToT), a method for aligning Cross-lingual CoT reasoning across languages. The proposed method, through a self-consistent cross-lingual prompting mechanism inspired by the Tree-of-Thoughts approach, provides multi-step reasoning paths in different languages that, during the steps, lead to the final solution. Experimental evaluations show that our method significantly outperforms existing prompting methods by reducing the number of interactions and achieving state-of-the-art performance.

翻译：推理方法，以广为人知的思维链（Chain-of-Thought, CoT）为代表，通过引导大型语言模型（LLMs）以逐步方式解决复杂任务，增强了其推理能力。尽管这些方法取得了显著成功，但由于预训练数据分布的不平衡，多步推理能力仍局限于英语，这使得其他语言成为障碍。本文提出跨语言思维树（Cross-lingual Tree-of-Thoughts, Cross-ToT），一种跨语言对齐CoT推理的方法。该方法受思维树启发，通过自洽的跨语言提示机制，在不同语言中提供多步推理路径，引导最终解决方案。实验评估表明，我们的方法通过减少交互次数并实现最先进性能，显著优于现有提示方法。