Math word problems (MWPs) require analyzing text descriptions and generating mathematical equations to derive solutions. Existing works focus on solving MWPs with two types of solvers: tree-based solver and large language model (LLM) solver. However, these approaches always solve MWPs by a single solver, which will bring the following problems: (1) Single type of solver is hard to solve all types of MWPs well. (2) A single solver will result in poor performance due to over-fitting. To address these challenges, this paper utilizes multiple ensemble approaches to improve MWP-solving ability. Firstly, We propose a problem type classifier that combines the strengths of the tree-based solver and the LLM solver. This ensemble approach leverages their respective advantages and broadens the range of MWPs that can be solved. Furthermore, we also apply ensemble techniques to both tree-based solver and LLM solver to improve their performance. For the tree-based solver, we propose an ensemble learning framework based on ten-fold cross-validation and voting mechanism. In the LLM solver, we adopt self-consistency (SC) method to improve answer selection. Experimental results demonstrate the effectiveness of these ensemble approaches in enhancing MWP-solving ability. The comprehensive evaluation showcases improved performance, validating the advantages of our proposed approach. Our code is available at this url: https://github.com/zhouzihao501/NLPCC2023-Shared-Task3-ChineseMWP.

翻译：数学应用题（MWPs）需要分析文本描述并生成数学方程以推导出解。现有研究主要采用两类求解器解决MWPs：基于树的求解器和大型语言模型（LLM）求解器。然而，这些方法始终使用单一求解器处理MWPs，这将带来以下问题：(1)单一类型的求解器难以完美求解所有类型的MWPs；(2)单一求解器会因过拟合导致性能不佳。为应对这些挑战，本文采用多种集成方法来提升MWP求解能力。首先，我们提出一种结合基于树的求解器与LLM求解器优势的问题类型分类器。这种集成方法充分利用了各自优势，扩展了可求解的MWPs范围。此外，我们还将集成技术分别应用于基于树的求解器和LLM求解器以提升其性能。针对基于树的求解器，我们提出基于十折交叉验证与投票机制的集成学习框架。在LLM求解器中，我们采用自一致性（SC）方法改善答案选择。实验结果证明了这些集成方法在增强MWP求解能力方面的有效性。全面评估展示了性能提升，验证了我们所提出方法的优势。我们的代码可通过以下网址获取：https://github.com/zhouzihao501/NLPCC2023-Shared-Task3-ChineseMWP。