Solutions to math word problems (MWPs) with step-by-step explanations are valuable, especially in education, to help students better comprehend problem-solving strategies. Most existing approaches only focus on obtaining the final correct answer. A few recent approaches leverage intermediate solution steps to improve final answer correctness but often cannot generate coherent steps with a clear solution strategy. Contrary to existing work, we focus on improving the correctness and coherence of the intermediate solutions steps. We propose a step-by-step planning approach for intermediate solution generation, which strategically plans the generation of the next solution step based on the MWP and the previous solution steps. Our approach first plans the next step by predicting the necessary math operation needed to proceed, given history steps, then generates the next step, token-by-token, by prompting a language model with the predicted math operation. Experiments on the GSM8K dataset demonstrate that our approach improves the accuracy and interpretability of the solution on both automatic metrics and human evaluation.

翻译：数学应用题的求解过程与逐步解释对教育领域尤为珍贵，能帮助学生更深入理解解题策略。现有方法大多仅关注获取最终正确答案，少数近期研究虽利用中间解题步骤提升最终答案正确性，但往往难以生成具有清晰解题策略的连贯步骤。与现有工作不同，我们致力于提升中间解题步骤的正确性与连贯性。本文提出一种基于分步规划的中间解题步骤生成方法，该方法能根据数学应用题及已生成的解题步骤，策略性地规划下一步骤的生成。具体而言，我们的方法首先通过预测历史步骤中所需的数学运算来规划下一步操作，随后以该预测运算为提示，通过语言模型逐词生成后续步骤。在GSM8K数据集上的实验表明，该方法在自动评估指标与人工评测中均显著提升了解题步骤的准确性与可解释性。