"Which LLM should I use?": Evaluating LLMs for tasks performed by Undergraduate Computer Science Students

TL;DR

This paper systematically evaluates multiple large language models to determine their effectiveness for tasks commonly performed by undergraduate computer science students, providing guidance on model selection.

Contribution

It offers a comprehensive comparison of popular LLMs across diverse student tasks, filling a gap in computing education research.

Findings

Google Bard and ChatGPT excel in code explanation and documentation

GitHub Copilot Chat performs best in coding tasks

Models show varied strengths in learning and communication tasks

Abstract

This study evaluates the effectiveness of various large language models (LLMs) in performing tasks common among undergraduate computer science students. Although a number of research studies in the computing education community have explored the possibility of using LLMs for a variety of tasks, there is a lack of comprehensive research comparing different LLMs and evaluating which LLMs are most effective for different tasks. Our research systematically assesses some of the publicly available LLMs such as Google Bard, ChatGPT(3.5), GitHub Copilot Chat, and Microsoft Copilot across diverse tasks commonly encountered by undergraduate computer science students in India. These tasks include code explanation and documentation, solving class assignments, technical interview preparation, learning new concepts and frameworks, and email writing. Evaluation for these tasks was carried out by pre-final year and final year undergraduate computer science students and provides insights into the models' strengths and limitations. This study aims to guide students as well as instructors in selecting suitable LLMs for any specific task and offers valuable insights on how LLMs can be used constructively by students and instructors.