MATHWELL: Generating Age-Appropriate Educational Math Word Problems
arxiv(2024)
摘要
Math word problems are critical K-8 educational tools, but writing them is
time-consuming and requires domain expertise. We suggest that language models
can support K-8 math education by automatically generating problems. To be
educational, generated problems must be 1) solvable, 2) accurate, and 3)
appropriate. Existing datasets are unlabeled for these criteria, making them
ill-suited for training problem generators. To address this gap, we use domain
expert annotation to curate a high-quality synthetic training dataset for this
task. We show the value of this data by using it to iteratively finetune
Llama-2 (70B) to create MATHWELL, a K-8 word problem generator. Domain experts
find MATHWELL has a 40
and meet all criteria than existing open-source models, with 74
problems with executable solutions being solvable, accurate, and appropriate.
MATHWELL achieves 94.9
outputting problems written at a more appropriate reading level for K-8
students. MATHWELL's performance despite being trained by finetuning only
highlights the quality of our synthetic data for training age-appropriate word
problem generators. We release our model, data, and annotations.
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