[R] A neural network solves, explains, and generates university math problems by program synthesis and few-shot learning at human level - Stanford 2022

Paper: [https://www.pnas.org/doi/full/10.1073/pnas.2123433119](https://www.pnas.org/doi/full/10.1073/pnas.2123433119)

Github: [https://github.com/idrori/mathQ](https://github.com/idrori/mathQ)

Abstract:

>We demonstrate that a neural network pretrained on text and fine-tuned on code solves mathematics course problems, explains solutions, and generates questions at a human level. We automatically synthesize programs using few-shot learning and OpenAI’s Codex transformer and execute them to solve course problems at 81% automatic accuracy. We curate a dataset of questions from Massachusetts Institute of Technology (MIT)’s largest mathematics courses (Single Variable and Multivariable Calculus, Differential Equations, Introduction to Probability and Statistics, …

few-shot learning human learning machinelearning math network neural network stanford university