SoT: Structured-of-Thought Prompting Guides Multilingual Reasoning in Large Language Models

• URL: http://arxiv.org/abs/2510.02648v1
• Date: Fri, 03 Oct 2025 01:02:14 GMT
• Title: SoT: Structured-of-Thought Prompting Guides Multilingual Reasoning in Large Language Models
• Authors: Rui Qi, Zhibo Man, Yufeng Chen, Fengran Mo, Jinan Xu, Kaiyu Huang,
• Abstract summary: We propose a training-free method that improves the performance on multilingual reasoning through a multi-step transformation.<n>The Structured-of-Thought (SoT) method converts language-specific semantic information into language-agnostic structured representations.<n> Experimental results demonstrate that SoT outperforms several strong baselines on multiple multilingual reasoning benchmarks.
• Score: 39.13376984049828
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent developments have enabled Large Language Models (LLMs) to engage in complex reasoning tasks through deep thinking. However, the capacity of reasoning has not been successfully transferred to non-high-resource languages due to resource constraints, which struggles with multilingual reasoning tasks. To this end, we propose Structured-of-Thought (SoT), a training-free method that improves the performance on multilingual reasoning through a multi-step transformation: Language Thinking Transformation and Structured Knowledge Transformation. The SoT method converts language-specific semantic information into language-agnostic structured representations, enabling the models to understand the query in different languages more sophisticated. Besides, SoT effectively guides LLMs toward more concentrated reasoning to maintain consistent underlying reasoning pathways when handling cross-lingual variations in expression. Experimental results demonstrate that SoT outperforms several strong baselines on multiple multilingual reasoning benchmarks when adapting to various backbones of LLMs. It can also be integrated with other training-free strategies for further improvements. Our code is available at https://github.com/Cherry-qwq/SoT.

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