Exploring the MIT Mathematics and EECS Curriculum Using Large Language
  Models
Authors: name: Sarah J. Zhang, name: Samuel Florin, name: Ariel N. Lee, name: Eamon Niknafs, name: Andrei Marginean, name: Annie Wang, name: Keith Tyser, name: Zad Chin, name: Yann Hicke, name: Nikhil Singh, name: Madeleine Udell, name: Yoon Kim, name: Tonio Buonassisi, name: Armando Solar-Lezama, name: Iddo Drori

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.