Chain-of-thought (CoT) is capable of eliciting models to explicitly generate reasoning paths, thus promoting reasoning accuracy and attracting increasing attention. Specifically, zero-shot CoT achieves remarkable improvements in a wide range of reasoning tasks by simply instructing the LLM with the prompt "Let's think step by step!". Despite the success of zero-shot CoT, the existing zero-shot prompting techniques remain limited to a single language, making it challenging to generalize to other languages and hindering global development. In this work, we introduce cross-lingual prompting (CLP), aiming to improve zero-shot CoT reasoning across languages. Specifically, CLP consists of two main components: (1) cross-lingual alignment prompting and (2) task-specific solver prompting. The cross-lingual alignment prompting is responsible for aligning representations across different languages, whereas the task-specific solver prompting is used to generate the final chain of thoughts and results for the reasoning task. In addition, we further introduce cross-lingual self-consistent prompting (CLSP) to ensemble different reasoning paths across languages. Our experimental evaluations on several benchmarks demonstrate that CLP and CLSP significantly outperform the existing prompting methods and achieve state-of-the-art performance. We hope this work will inspire further breakthroughs in cross-lingual CoT.

翻译：思维链（CoT）能够引导模型显式生成推理路径，从而提升推理准确性并引发广泛关注。具体而言，零样本CoT通过简单地指导大语言模型使用提示“让我们一步步思考！”在多种推理任务中取得了显著改进。尽管零样本CoT取得了成功，但现有的零样本提示技术仍局限于单一语言，难以推广至其他语言，阻碍了全球发展。本文提出跨语言提示（CLP），旨在提升跨语言的零样本CoT推理。具体而言，CLP包含两个主要部分：（1）跨语言对齐提示和（2）任务特定求解提示。跨语言对齐提示负责对齐不同语言的表示，而任务特定求解提示用于生成推理任务的最终思维链和结果。此外，我们进一步引入跨语言自一致性提示（CLSP），以集成跨语言的不同推理路径。在多个基准上的实验评估表明，CLP和CLSP显著优于现有提示方法，并达到了最先进性能。我们希望这项工作能激发跨语言CoT的进一步突破。