@inproceedings{liang-etal-2023-gpt,
title = "Let {GPT} be a Math Tutor: Teaching Math Word Problem Solvers with Customized Exercise Generation",
author = "Liang, Zhenwen and
Yu, Wenhao and
Rajpurohit, Tanmay and
Clark, Peter and
Zhang, Xiangliang and
Kalyan, Ashwin",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.889",
doi = "10.18653/v1/2023.emnlp-main.889",
pages = "14384--14396",
abstract = "In this paper, we present a novel approach for distilling math word problem solving capabilities from large language models (LLMs) into smaller, more efficient student models. Our approach is designed to consider the student model{'}s weaknesses and foster a tailored learning experience by generating targeted exercises aligned with educational science principles, such as knowledge tracing and personalized learning. Concretely, we let GPT-3 be a math tutor and run two steps iteratively: 1) assessing the student model{'}s current learning status on a GPT-generated exercise book, and 2) improving the student model by training it with tailored exercise samples generated by GPT-3. Experimental results reveal that our approach outperforms LLMs (e.g., GPT-3 and PaLM) in accuracy across three distinct benchmarks while employing significantly fewer parameters. Furthermore, we provide a comprehensive analysis of the various components within our methodology to substantiate their efficacy.",
}
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%0 Conference Proceedings
%T Let GPT be a Math Tutor: Teaching Math Word Problem Solvers with Customized Exercise Generation
%A Liang, Zhenwen
%A Yu, Wenhao
%A Rajpurohit, Tanmay
%A Clark, Peter
%A Zhang, Xiangliang
%A Kalyan, Ashwin
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F liang-etal-2023-gpt
%X In this paper, we present a novel approach for distilling math word problem solving capabilities from large language models (LLMs) into smaller, more efficient student models. Our approach is designed to consider the student model’s weaknesses and foster a tailored learning experience by generating targeted exercises aligned with educational science principles, such as knowledge tracing and personalized learning. Concretely, we let GPT-3 be a math tutor and run two steps iteratively: 1) assessing the student model’s current learning status on a GPT-generated exercise book, and 2) improving the student model by training it with tailored exercise samples generated by GPT-3. Experimental results reveal that our approach outperforms LLMs (e.g., GPT-3 and PaLM) in accuracy across three distinct benchmarks while employing significantly fewer parameters. Furthermore, we provide a comprehensive analysis of the various components within our methodology to substantiate their efficacy.
%R 10.18653/v1/2023.emnlp-main.889
%U https://aclanthology.org/2023.emnlp-main.889
%U https://doi.org/10.18653/v1/2023.emnlp-main.889
%P 14384-14396
Markdown (Informal)
[Let GPT be a Math Tutor: Teaching Math Word Problem Solvers with Customized Exercise Generation](https://aclanthology.org/2023.emnlp-main.889) (Liang et al., EMNLP 2023)
ACL