Empowering Multi-step Reasoning across Languages via Program-Aided Language Models

In-context learning methods are commonly employed as inference strategies, where Large Language Models (LLMs) are elicited to solve a task by leveraging provided demonstrations without requiring parameter updates. Among these approaches are the reasoning methods, exemplified by Chain-of-Thought (CoT) and Program-Aided Language Models (PAL), which encourage LLMs to generate reasoning steps, leading to improved accuracy. Despite their success, the ability to deliver multi-step reasoning remains limited to a single language, making it challenging to generalize to other languages and hindering global development. In this work, we propose Cross-lingual Program-Aided Language Models (CrossPAL), a method for aligning reasoning programs across languages. Our method delivers programs as intermediate reasoning steps in different languages through a double-step cross-lingual prompting mechanism inspired by the Program-Aided approach. Moreover, we introduce Self-consistent Cross-PAL (SCross-PAL) to ensemble different reasoning paths across languages. Our experimental evaluations show that Cross-PAL outperforms existing methods, reducing the number of interactions and achieving state-of-the-art performance.