通过思维树赋能跨语言多步推理
Empowering Multi-step Reasoning across Languages via Tree-of-Thoughts
AI总结：
针对大语言模型多步推理能力受限于英语的问题，提出受思维树启发的Cross-ToT跨语言推理方法，通过自洽跨语言提示提供多语言推理路径，减少交互次数并达到最优性能。
摘要：
以知名的Chain-of-Thought（CoT，思维链）为典型代表的推理方法，通过引导大语言模型（LLMs）逐步解决复杂任务来增强其推理能力。尽管这类方法已取得显著成效，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，其他语言成为了阻碍。本文提出Cross-lingual Tree-of-Thoughts（Cross-ToT，跨语言思维树），这是一种用于对齐跨语言CoT推理的方法。该方法受思维树方法启发，通过自洽的跨语言提示机制，提供不同语言下的多步推理路径，这些路径在逐步推导过程中导向最终解决方案。实验评估表明，我们的方法显著优于现有提示方法，减少了交互次数，并达到了当前最优性能。
英文摘要：
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.
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。
