Solving Math Word Problems concerning Systems of Equations with GPT-3

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LLM/Chat, Generative AI

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• Researchers have been interested in developing AI tools to help students learn various mathematical subjects.

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• One chal- lenging set of tasks for school students is learning to solve math word problems.

• One chal- lenging set of tasks for school students is learning to solve math word problems.
• We explore how recent advances in natural language processing, specifically the rise of power- ful transformer based models, can be applied to help math learners with such problems.

Researchers have been interested in developing AI tools to help students learn various mathematical subjects. One chal- lenging set of tasks for school students is learning to solve math word problems.

• Require human review of generated outputs and explicit guidance against over-reliance or answer copying.

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Medium

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Solving Math Word Problems concerning Systems of Equations with GPT-3

• Mingyu Zong
• Bhaskar Krishnamachari

Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37 No. 13, EAAI-23

2023

10.1609/aaai.v37i13.26896

https://ojs.aaai.org/index.php/AAAI/article/view/26896

https://ojs.aaai.org/index.php/AAAI/article/view/26896/26668

087_Solving Math Word Problems Concerning Systems of Equations with GPT-3.pdf

8

Researchers have been interested in developing AI tools to help students learn various mathematical subjects. One chal- lenging set of tasks for school students is learning to solve math word problems. We explore how recent advances in natural language processing, specifically the rise of power- ful transformer based models, can be applied to help math learners with such problems. Concretely, we evaluate the use of GPT-3, a 1.75B parameter transformer model recently re- leased by OpenAI, for three related challenges pertaining to math word problems corresponding to systems of two linear equations. The three challenges are classifying word prob- lems, extracting equations from word problems, and generat- ing word problems. For the first challenge, we define a set of problem classes and find that GPT-3 has generally very high accuracy in classifying word problems (80%-100%), for all but one of these classes. For the second challenge, we find the accuracy for extracting equations improves with number of examples provided to the model, ranging from an accu- racy of 31% for zero-shot learning to about 69% using 3-shot learning, which is further improved to a high value of 80% with fine-tuning. For the third challenge, we find that GPT-3 is able to generate problems with accuracy ranging from 33% to 93%, depending on the problem type.

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Infrastructure depends on AI tool type, learner devices, data access, and institutional policy context.

High

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