CSEPrompts: A Benchmark of Introductory Computer Science Prompts
Abstract: Recent advances in AI, machine learning, and NLP have led to the development of a new generation of LLMs that are trained on massive amounts of data and often have trillions of parameters. Commercial applications (e.g., ChatGPT) have made this technology available to the general public, thus making it possible to use LLMs to produce high-quality texts for academic and professional purposes. Schools and universities are aware of the increasing use of AI-generated content by students and they have been researching the impact of this new technology and its potential misuse. Educational programs in Computer Science (CS) and related fields are particularly affected because LLMs are also capable of generating programming code in various programming languages. To help understand the potential impact of publicly available LLMs in CS education, we introduce CSEPrompts, a framework with hundreds of programming exercise prompts and multiple-choice questions retrieved from introductory CS and programming courses. We also provide experimental results on CSEPrompts to evaluate the performance of several LLMs with respect to generating Python code and answering basic computer science and programming questions.
- OpenAI, GPT-4 Technical Report. ArXiv abs/2303.08774 (2023)
- The MosaicML NLP Team, MPT-30B: Raising the bar for open-source foundation models. (2023)
- Anel Islamovic, Stability AI Launches the First of its StableLM Suite of Language Models. (2023)
- Stella Biderman, Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling. EleutherAI (2023)
- holger krekel and pytest-dev team, pytest: helps you write better programs. (2023)
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.
