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    <abstract>As large language models (LLMs) have shown effectiveness with different prompting methods, such as Chain of Thought, Program of Thought, we find that these methods have formed a great complementarity to each other on math reasoning tasks. In this work, we propose XoT, an integrated problem solving framework by prompting LLMs with diverse reasoning thoughts. For each question, XoT always begins with selecting the most suitable method then executes each method iteratively. Within each iteration, XoT actively checks the validity of the generated answer and incorporates the feedback from external executors, allowing it to dynamically switch among different prompting methods. Through extensive experiments on 10 popular math reasoning datasets, we demonstrate the effectiveness of our proposed approach and thoroughly analyze the strengths of each module. Moreover, empirical results suggest that our framework is orthogonal to recent work that makes improvements on single reasoning methods and can further generalise to logical reasoning domain. By allowing method switching, XoT provides a fresh perspective on the collaborative integration of diverse reasoning thoughts in a unified framework.</abstract>
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%0 Conference Proceedings
%T Plan, Verify and Switch: Integrated Reasoning with Diverse X-of-Thoughts
%A Liu, Tengxiao
%A Guo, Qipeng
%A Yang, Yuqing
%A Hu, Xiangkun
%A Zhang, Yue
%A Qiu, Xipeng
%A Zhang, Zheng
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F liu-etal-2023-plan
%X As large language models (LLMs) have shown effectiveness with different prompting methods, such as Chain of Thought, Program of Thought, we find that these methods have formed a great complementarity to each other on math reasoning tasks. In this work, we propose XoT, an integrated problem solving framework by prompting LLMs with diverse reasoning thoughts. For each question, XoT always begins with selecting the most suitable method then executes each method iteratively. Within each iteration, XoT actively checks the validity of the generated answer and incorporates the feedback from external executors, allowing it to dynamically switch among different prompting methods. Through extensive experiments on 10 popular math reasoning datasets, we demonstrate the effectiveness of our proposed approach and thoroughly analyze the strengths of each module. Moreover, empirical results suggest that our framework is orthogonal to recent work that makes improvements on single reasoning methods and can further generalise to logical reasoning domain. By allowing method switching, XoT provides a fresh perspective on the collaborative integration of diverse reasoning thoughts in a unified framework.
%R 10.18653/v1/2023.emnlp-main.169
%U https://aclanthology.org/2023.emnlp-main.169
%U https://doi.org/10.18653/v1/2023.emnlp-main.169
%P 2807-2822

Markdown (Informal)

[Plan, Verify and Switch: Integrated Reasoning with Diverse X-of-Thoughts](https://aclanthology.org/2023.emnlp-main.169) (Liu et al., EMNLP 2023)

• Plan, Verify and Switch: Integrated Reasoning with Diverse X-of-Thoughts (Liu et al., EMNLP 2023)

ACL

• Tengxiao Liu, Qipeng Guo, Yuqing Yang, Xiangkun Hu, Yue Zhang, Xipeng Qiu, and Zheng Zhang. 2023. Plan, Verify and Switch: Integrated Reasoning with Diverse X-of-Thoughts. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 2807–2822, Singapore. Association for Computational Linguistics.