Interpretable Math Word Problem Solution Generation Via Step-by-step Planning
This addresses the need for better educational tools by enhancing interpretability in math problem-solving, though it is incremental as it builds on existing methods for intermediate steps.
The paper tackles the problem of generating coherent and correct step-by-step solutions for math word problems, proposing a planning approach that predicts the next math operation before generating each step, which improved accuracy and interpretability on the GSM8K dataset.
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.