Integrating Type Systems into Neural Network Training for Reliable Code Generation

Summary

The article discusses the limitations of current neural network approaches to code generation, particularly how Large Language Models (LLMs) are trained to produce token sequences rather than type-correct code. It explores the disconnect between training and typechecking, and proposes that future models should be trained to directly generate type-correct code by integrating type systems into the training process. The article suggests this could lead to more reliable code generation for strongly-typed languages like Idris, Lean, and Agda.