Cross-lingual Prompting: Improving Zero-shot Chain-of-Thought Reasoning
  across Languages
Libo Qin Qiguang Chen Fuxuan Wei Shijue Huang Wanxiang Che
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.