Empowering Multi-step Reasoning across Languages via Tree-of-Thoughts

• URL: http://arxiv.org/abs/2311.08097v4
• Date: Fri, 21 Jun 2024 06:06:51 GMT
• Title: Empowering Multi-step Reasoning across Languages via Tree-of-Thoughts
• Authors: Leonardo Ranaldi, Giulia Pucci, Federico Ranaldi, Elena Sofia Ruzzetti, Fabio Massimo Zanzotto,
• Abstract summary: Chain-of-Thought (CoT) methods empower Large Language Models (LLMs) to solve complex tasks in a step-by-step manner. The ability to deliver multi-step reasoning remains limited to English because of the imbalance in the distribution of pre-training data. We propose Cross-lingual Tree-of-Thoughts (Cross-ToT), a method for aligning Cross-lingual CoT reasoning across languages.
• Score: 1.8175282137722093
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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