Evaluating Large Language Models Trained on Code

This paper explores the capabilities of large language models, specifically Codex, when trained on a massive dataset of code. It evaluates how well these models can generate functional code from natural language descriptions, highlighting their strengths and limitations. The research demonstrates the potential of using such models for code generation and software development tasks, while also discussing the challenges in ensuring code correctness and reliability.