Plan, Verify and Switch: Integrated Reasoning with Diverse X-of-Thoughts

TL;DR

XoT is an integrated reasoning framework for large language models that dynamically switches among diverse prompting methods, improving math reasoning performance through iterative validation and feedback incorporation.

Contribution

This work introduces XoT, a novel framework that combines multiple reasoning methods with dynamic switching and validation, enhancing LLM problem-solving capabilities.

Findings

Outperforms existing single-method prompting approaches on 10 math datasets.

Demonstrates the effectiveness of method switching and validation in reasoning tasks.

Generalizes well to logical reasoning domains.

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. The code is available at https://github.com/tengxiaoliu/XoT.