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（以著名的思维链（CoT）为典范）通过引导大型语言模型（LLM）以逐步方式解决复杂任务，显著增强了其推理能力。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均衡，多步推理能力目前仍主要局限于英语，其他语言成为推理障碍。本文提出跨语言思维树（Cross-ToT），一种实现跨语言CoT推理对齐的方法。该方法受思维树（Tree-of-Thoughts）方法启发，通过自洽的跨语言提示机制，在推理过程中生成指向最终解决方案的不同语言多步推理路径。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的性能，显著优于现有的提示方法。