Exploring the MIT Mathematics and EECS Curriculum Using Large Language
  Models
We curate a comprehensive dataset of 4,550 questions and solutions from
problem sets, midterm exams, and final exams across all MIT Mathematics and
Electrical Engineering and Computer Science (EECS) courses required for
obtaining a degree. We evaluate the ability of large language models to fulfill
the graduation requirements for any MIT major in Mathematics and EECS. Our
results demonstrate that GPT-3.5 successfully solves a third of the entire MIT
curriculum, while GPT-4, with prompt engineering, achieves a perfect solve rate
on a test set excluding questions based on images. We fine-tune an open-source
large language model on this dataset. We employ GPT-4 to automatically grade
model responses, providing a detailed performance breakdown by course,
question, and answer type. By embedding questions in a low-dimensional space,
we explore the relationships between questions, topics, and classes and
discover which questions and classes are required for solving other questions
and classes through few-shot learning. Our analysis offers valuable insights
into course prerequisites and curriculum design, highlighting language models'
potential for learning and improving Mathematics and EECS education.
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Sarah J. Zhang
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Samuel Florin
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Sam Florin
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Ariel N. Lee
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2023-06-16T02:36:08.413Z
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Ariel N. Lee
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Eamon Niknafs
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Eamon
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Andrei Marginean
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Annie Wang
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Keith Tyser
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Zad Chin
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Yann Hicke
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Nikhil Singh
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2023-06-16T07:56:07.095Z
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Nikhil Singh
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Madeleine Udell
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2025-04-27T22:31:25.057Z
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Madeleine Udell
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Yoon Kim
extracted_pending
2023-06-16T02:05:38.166Z
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Yoon Kim
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Tonio Buonassisi
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Armando Solar-Lezama
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Iddo Drori
claimed_verified
2023-06-16T13:57:32.533Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/1657402192472-62c9f32a334a6ee11c35d601.png
Iddo Drori
user
idrori
Exploring the MIT Mathematics and EECS Curriculum Using Large Language
  Models
We curate a comprehensive dataset of 4,550 questions and solutions from
problem sets, midterm exams, and final exams across all MIT Mathematics and
Electrical Engineering and Computer Science (EECS) courses required for
obtaining a degree. We evaluate the ability of large language models to fulfill
the graduation requirements for any MIT major in Mathematics and EECS. Our
results demonstrate that GPT-3.5 successfully solves a third of the entire MIT
curriculum, while GPT-4, with prompt engineering, achieves a perfect solve rate
on a test set excluding questions based on images. We fine-tune an open-source
large language model on this dataset. We employ GPT-4 to automatically grade
model responses, providing a detailed performance breakdown by course,
question, and answer type. By embedding questions in a low-dimensional space,
we explore the relationships between questions, topics, and classes and
discover which questions and classes are required for solving other questions
and classes through few-shot learning. Our analysis offers valuable insights
into course prerequisites and curriculum design, highlighting language models'
potential for learning and improving Mathematics and EECS education.
Large language models, including GPT-3.5 and GPT-4, can effectively solve a significant portion of MIT Mathematics and EECS curriculum questions, with GPT-4 achieving near-perfect performance through prompt engineering and fine-tuning.
large language models GPT-3.5 GPT-4 prompt engineering fine-tuning few-shot learning