A Tree-of-Thoughts to Broaden Multi-step Reasoning across Languages

Leonardo Ranaldi, Giulia Pucci, Federico Ranaldi, Elena Sofia Ruzzetti, Fabio Massimo Zanzotto


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.
Anthology ID:
2024.findings-naacl.78
Volume:
Findings of the Association for Computational Linguistics: NAACL 2024
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1229–1241
Language:
URL:
https://aclanthology.org/2024.findings-naacl.78
DOI:
Bibkey:
Cite (ACL):
Leonardo Ranaldi, Giulia Pucci, Federico Ranaldi, Elena Sofia Ruzzetti, and Fabio Massimo Zanzotto. 2024. A Tree-of-Thoughts to Broaden Multi-step Reasoning across Languages. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 1229–1241, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
A Tree-of-Thoughts to Broaden Multi-step Reasoning across Languages (Ranaldi et al., Findings 2024)
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PDF:
https://aclanthology.org/2024.findings-naacl.78.pdf
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 2024.findings-naacl.78.copyright.pdf