规划、验证与切换：多样化思维链的集成推理
Plan, Verify and Switch: Integrated Reasoning with Diverse X-of-Thoughts
AI总结：
提出XoT框架，通过动态选择、验证和切换多种思维提示方法，集成推理以提升数学与逻辑推理性能。
摘要：
随着大型语言模型（LLMs）在不同提示方法（如思维链、程序化思维）中展现出有效性，我们发现这些方法在数学推理任务上形成了很强的互补性。在本工作中，我们提出了XoT，一个通过向LLMs提示多样化推理思维来集成问题求解的框架。对于每个问题，XoT总是先选择最合适的方法，然后迭代执行每种方法。在每次迭代中，XoT主动检查生成答案的有效性，并整合来自外部执行器的反馈，使其能够动态地在不同提示方法之间切换。通过在10个流行的数学推理数据集上的大量实验，我们证明了所提出方法的有效性，并深入分析了每个模块的优势。此外，实证结果表明，我们的框架与近期在单一推理方法上改进的工作是正交的，并能进一步推广到逻辑推理领域。通过允许方法切换，XoT为在统一框架中协作集成多样化推理思维提供了新视角。代码可在https://github.com/tengxiaoliu/XoT获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
