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In this paper, we present a novel approach for distilling math word problemsolving capabilities from large language models (LLMs) into smaller, moreefficient student models. Our approach is designed to consider the studentmodel's weaknesses and foster a tailored learning experience by generatingtargeted exercises aligned with educational science principles, such asknowledge tracing and personalized learning. Concretely, we let GPT-3 be a mathtutor and run two steps iteratively: 1) assessing the student model's currentlearning status on a GPT-generated exercise book, and 2) improving the studentmodel by training it with tailored exercise samples generated by GPT-3.Experimental results reveal that our approach outperforms LLMs (e.g., GPT-3 andPaLM) in accuracy across three distinct benchmarks while employingsignificantly fewer parameters. Furthermore, we provide a comprehensiveanalysis of the various components within our methodology to substantiate theirefficacy.

Let GPT be a Math Tutor: Teaching Math Word Problem Solvers with Customized Exercise Generation