Exploring the MIT Mathematics and EECS Curriculum Using Large Language Models

Understand

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.