Empowering Multi-step Reasoning across Languages via Program-Aided Language Models
Abstract: In-context learning methods are popular inference strategies where Large Language Models (LLMs) are elicited to solve a task using provided demonstrations without parameter updates.
Among these approaches are the reasoning methods, best exemplified by Chain-of-Thought (CoT) and Program-Aided Language Models (PAL), which elicit LLMs to generate reasoning paths, thus promoting accuracy and attracting increasing attention. However, despite the success of these methods, 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.
In particular, 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. In addition, we introduce Self-consistent CrossPAL (SCrossPAL) to ensemble different reasoning paths across languages. Our experimental evaluations show that our method significantly outperforms existing prompting methods, reducing the number of interactions and achieving state-of-the-art performance.
Paper Type: Long
Research Area: Multilingualism and Cross-Lingual NLP
Research Area Keywords: Multilingual Reasoning Methods
Contribution Types: Model analysis & interpretability, NLP engineering experiment, Reproduction study, Approaches to low-resource settings
Languages Studied: Bengali , Chinese , French , Thai , German , Japanese , Russian , Telugu,Spanish , Swahili
Submission Number: 1634
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