Empowering Multi-step Reasoning across Languages via Tree-of-Thoughts
Leonardo Ranaldi Fabio Massimo Zanzotto
Chain-of-Thought (CoT) prompting empowers the reasoning abilities of Large
Language Models (LLMs), eliciting them to solve complex reasoning tasks
step-by-step. However, with the success of CoT methods, the ability to deliver
multi-step reasoning remains limited to English due to the imbalance in the
distribution of the pre-training data, making the other languages a barrier.
  In this work, we propose a Cross-lingual multi-step reasoning approach,
aiming to align reasoning processes across different languages. In particular,
our method, through a Self-consistent Cross-lingual prompting mechanism
inspired by the Tree-of-Thoughts approach, delivers multi-step reasoning paths
in different languages that, during the steps, lead to the final solution. Our
experimental evaluations show that our method significantly outperforms
existing prompting methods, reducing the number of interactions and achieving
state-of-the-art performance.