Generative Language Models for Automated Programming Feedback

Abstract: In recent years, Generative Language Models have exploded into the mainstream with household names like BERT and ChatGPT, proving that text generation could have the potential to solve a variety of tasks. As the number of students enrolled into programming classes has increased significantly, providing adequate feedback for everyone has become a pressing logistical issue. In this work, we evaluate the ability of near state-of-the-art Generative Language Models to provide said feedback on an automated basis. Our results show that the latest publicly available model GPT-3.5 has a significant aptitude for finding errors in code while the older GPT-3 is noticeably more uneven in its analysis. It is our hope that future, potentially fine-tuned models could help fill the role of providing early feedback for beginners, thus significantly alleviating the pressure put upon instructors.