Empowering Multi-step Reasoning across Languages via Tree-of-Thoughts
arxiv(2023)
摘要
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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