"Which LLM should I use?": Evaluating LLMs for tasks performed by Undergraduate Computer Science Students
It addresses the lack of comprehensive comparisons of LLMs for specific educational tasks, providing practical guidance for students and instructors, though it is incremental as it applies existing models to a new context.
This study systematically evaluated publicly available large language models (LLMs) like Google Bard and ChatGPT(3.5) on tasks such as code explanation and solving assignments for undergraduate computer science students in India, finding insights into their strengths and limitations to guide selection.
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.