An Empirical Study on Challenging Math Problem Solving with GPT-4
Employing Large Language Models (LLMs) to address mathematical problems is an
intriguing research endeavor, considering the abundance of math problems
expressed in natural language across numerous science and engineering fields.
While several prior works have investigated solving elementary mathematics
using LLMs, this work explores the frontier of using GPT-4 for solving more
complex and challenging math problems. We evaluate various ways of using GPT-4.
Some of them are adapted from existing work, and one is \MathChat, a
conversational problem-solving framework newly proposed in this work. We
perform the evaluation on difficult high school competition problems from the
MATH dataset, which shows the advantage of the proposed conversational
approach.
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Yiran Wu
claimed_verified
2023-06-06T08:38:58.448Z
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Yiran Wu
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kevinwyr
647d3dab83cdb93baf76eddd
Feiran Jia
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2023-06-06T08:38:55.161Z
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Feiran Jia
user
jtongxin
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Shaokun Zhang
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2025-05-13T08:14:26.417Z
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shaokun zhang
user
Shaokunkun
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Qingyun Wu
claimed_verified
2025-05-16T07:14:05.915Z
647fa485d0cd8be13e662973
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Qingyun Wu
user
qingyun-wu
647d3dab83cdb93baf76ede0
Hangyu Li
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Erkang Zhu
647d3dab83cdb93baf76ede2
Yue Wang
647d3dab83cdb93baf76ede3
Yin Tat Lee
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Richard Peng
647d3dab83cdb93baf76ede5
Chi Wang
claimed_verified
2023-06-06T08:38:52.167Z
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Chi Wang
user
sonichi
An Empirical Study on Challenging Math Problem Solving with GPT-4
Employing Large Language Models (LLMs) to address mathematical problems is an
intriguing research endeavor, considering the abundance of math problems
expressed in natural language across numerous science and engineering fields.
While several prior works have investigated solving elementary mathematics
using LLMs, this work explores the frontier of using GPT-4 for solving more
complex and challenging math problems. We evaluate various ways of using GPT-4.
Some of them are adapted from existing work, and one is \MathChat, a
conversational problem-solving framework newly proposed in this work. We
perform the evaluation on difficult high school competition problems from the
MATH dataset, which shows the advantage of the proposed conversational
approach.
A study evaluates the use of GPT-4 for solving complex mathematical problems using various methods, including a new conversational framework called MathChat, demonstrating its effectiveness on challenging high school competition problems.
Large Language Models LLMs GPT-4 MathChat conversational problem-solving framework MATH dataset