Performance of Large Language Models in a Computer Science Degree Program
This study assesses the effectiveness of LLMs as educational aids in computer science curricula, highlighting their potential and limitations for students and educators.
The paper evaluated the performance of large language models like ChatGPT-3.5, GPT-4.0, BingAI, and LLaMa in an undergraduate computer science degree program, finding that ChatGPT-3.5 averaged 79.9% of the total score across 10 modules, but even GPT-4.0 would not pass due to limitations in mathematical calculations.
Large language models such as ChatGPT-3.5 and GPT-4.0 are ubiquitous and dominate the current discourse. Their transformative capabilities have led to a paradigm shift in how we interact with and utilize (text-based) information. Each day, new possibilities to leverage the capabilities of these models emerge. This paper presents findings on the performance of different large language models in a university of applied sciences' undergraduate computer science degree program. Our primary objective is to assess the effectiveness of these models within the curriculum by employing them as educational aids. By prompting the models with lecture material, exercise tasks, and past exams, we aim to evaluate their proficiency across different computer science domains. We showcase the strong performance of current large language models while highlighting limitations and constraints within the context of such a degree program. We found that ChatGPT-3.5 averaged 79.9% of the total score in 10 tested modules, BingAI achieved 68.4%, and LLaMa, in the 65 billion parameter variant, 20%. Despite these convincing results, even GPT-4.0 would not pass the degree program - due to limitations in mathematical calculations.