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CSEPrompts: A Benchmark of Introductory Computer Science Prompts

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Published
  • Md Nishat Raihan
  • Dhiman Goswami
  • Sadiya Sayara Chowdhury Puspo
  • Christian Newman
  • Tharindu Ranasinghe
  • Marcos Zampieri
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Publication date17/06/2024
Host publicationFoundations of Intelligent Systems: 27th International Symposium, ISMIS 2024, Poitiers, France, June 17–19, 2024, Proceedings
EditorsAnnalisa Appice, Hanane Azzag, Mohand-Said Hacid, Allel Hadjali, Zbigniew Ras
Place of PublicationCham
PublisherSpringer Nature
Pages45-54
Number of pages10
ISBN (electronic)9783031627002
ISBN (print)9783031626999
<mark>Original language</mark>English
EventFoundations of Intelligent Systems: 27th International Symposium - Poitiers, France
Duration: 17/07/202419/07/2024

Conference

ConferenceFoundations of Intelligent Systems: 27th International Symposium
Country/TerritoryFrance
CityPoitiers
Period17/07/2419/07/24

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume14670

Conference

ConferenceFoundations of Intelligent Systems: 27th International Symposium
Country/TerritoryFrance
CityPoitiers
Period17/07/2419/07/24

Abstract

Recent advances in AI, machine learning, and NLP have led to the development of a new generation of Large Language Models (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 (https://github.com/mraihan-gmu/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.