Papers › MATHWELL: Generating Educational Math Word Problems Using Teacher Annotations

MATHWELL: Generating Educational Math Word Problems Using Teacher Annotations

24 Feb 2024arXiv:2402.15861archive 2025-07-28

Bryan R Christ, Jonathan Kropko, Thomas Hartvigsen

Math word problems are critical K-8 educational tools, but writing them is time consuming and requires extensive expertise. To be educational, problems must be solvable, have accurate answers, and, most importantly, be educationally appropriate. We propose that language models have potential to support K-8 math education by automatically generating word problems. However, evaluating educational appropriateness is hard to quantify. We fill this gap by having teachers evaluate problems generated by LLMs, who find existing models and data often fail to be educationally appropriate. We then explore automatically generating educational word problems, ultimately using our expert annotations to finetune a 70B language model. Our model, MATHWELL, is the first K-8 word problem generator targeted at educational appropriateness. Further expert studies find MATHWELL generates problems far more solvable, accurate, and appropriate than public models. MATHWELL also matches GPT-4's problem quality while attaining more appropriate reading levels for K-8 students and avoiding generating harmful questions.

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Code

bryanchrist/mathwell officialmentioned in papermentioned on GitHubpytorchGPL-3.0 report
bryanchrist/mathneuro mentioned on GitHubpytorch report

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Language ModelingLanguage ModellingMath

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Educational Grade School Math (EGSM)MATHWELL Human Annotation Dataset

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Methods

Absolute Position EncodingsAdamAttentionBPEDense ConnectionsDropoutGPT-4Label SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxTransformer

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