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.

本文展示了大型语言模型在解决麻省理工学院数学和电气工程与计算机科学课程期末试卷及期中考卷等问题时的表现，GPT-3.5 可成功解决了麻省理工学院三分之一的课程，而经过优化的 GPT-4 在去除图像题后表现完美；同时，作者将该数据集应用于语言模型的学习和评分，通过 few-shot learning 等发现了问题、题目和课程之间的关系，并探索了课程要求和课程设计等问题。