Plan, Verify and Switch: Integrated Reasoning with Diverse X-of-Thoughts (2310.14628v2)

Abstract: As 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.

Integrated Reasoning with Diverse X-of-Thoughts

The paper "Plan, Verify and Switch: Integrated Reasoning with Diverse X-of-Thoughts" introduces a novel framework to enhance the problem-solving capabilities of LLMs by leveraging the complementarity of distinct prompting strategies. The authors propose the XoT framework, an integrated system that orchestrates different reasoning methods, particularly for mathematical reasoning tasks. It strategically selects and switches between these methods, using both active and passive verification mechanisms to optimize performance.

Overview of XoT Framework

The XoT framework comprises three critical components: the planning module, the reasoning module, and the verification module. Each of these modules plays a specific role in iteratively solving problems by adapting various strategies:

1. Planning Module: This module is designed to select the most appropriate problem-solving method depending on the characteristics of the input question. The paper identifies multiple reasoning approaches, including Chain-of-Thought (CoT), Program-of-Thought (PoT), and the newly introduced Equation-of-Thought (EoT). The module capitalizes on the unique strengths of each approach to enhance reasoning efficiency.
2. Reasoning Module: Upon selection of a method by the planning module, the reasoning module generates a solution based on the chosen strategy. The CoT method crafts a step-by-step natural language rationale, PoT leverages Python programming to calculate precise answers, and EoT utilizes systems of linear equations to approach math problems directly.
3. Verification Module: This component is responsible for ensuring the validity of the generated solution. It employs passive verification through external program execution and active verification by reconsidering the solution against the problem's conditions. Unsuccessful verifications prompt the system to attempt another reasoning path.

Experimental Results

Extensive evaluations across ten mathematical reasoning datasets validate the effectiveness of the XoT framework. The system showcases robust performance enhancements over single-method approaches and demonstrates superior handling of complex reasoning tasks typically found in datasets like GSM8K, SVAMP, and others. Notably, the integration of active verification significantly reduces false-positive rates, thus allowing more frequent method switching under problematic scenarios.

The framework also exhibits versatility in accommodating recent advances in single reasoning methods, indicating its potential as a generalizable tool across diverse problem domains, including logic reasoning.

Implications and Future Directions

The implications of integrating multiple reasoning methods within a unified framework like XoT are far-reaching. It suggests a pathway for enhancing LLM capabilities in domains where traditional single-path reasoning might falter, such as scientific reasoning, technical problem-solving, and educational technologies requiring step-by-step logical construction.

Moreover, the adaptability of the XoT system to logical reasoning tasks, as indicated by experimentations on datasets like FOLIO, provides fertile ground for future research. Exploring additional reasoning strategies and enhancing planning algorithms for dynamic method selection could further improve the efficacy and applicability of this framework.

Conclusion

The XoT framework offers a promising direction in AI by integrating diverse reasoning thoughts and enabling efficient navigational switching between them through sophisticated verification processes. This methodological advancement not only contributes to the field of mathematical reasoning but also sets a precedent for developing future AI systems that demand high adaptability and precision across a spectrum of problem-solving scenarios. As the framework continues to evolve, it may significantly influence the operational paradigms of LLMs within complex and varied cognitive tasks.