Performance of Large Language Models in a Computer Science Degree
  Program
Tim Krüger Michael Gref
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.