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.

这篇论文在应用科学大专的计算机科学学士学位课程中研究了不同大型语言模型的表现和效果，发现当前的大型语言模型在不同计算机科学领域表现出色，但由于数学计算方面的限制，即使是 GPT-4.0 也无法通过该课程。