CSEPrompts: A Benchmark of Introductory Computer Science Programming Prompts and Multiple-Choice Questions

Core Concepts

CSEPrompts is a novel evaluation framework comprising hundreds of programming exercise prompts and multiple-choice questions from introductory computer science and programming courses, designed to assess the performance of state-of-the-art Large Language Models on tasks commonly encountered in CS education.

Abstract

The paper introduces CSEPrompts, a comprehensive evaluation framework for assessing the capabilities of Large Language Models (LLMs) on introductory computer science and programming tasks. The framework includes: 118 programming prompts curated from popular online coding platforms like CodingBat, LearnPython, Edabit, Python Principles, and HackerRank. 101 programming prompts from introductory computer science MOOCs offered by Harvard, University of Michigan, and Georgia Tech. 50 multiple-choice questions (MCQs) from the Georgia Tech MOOC courses. The authors evaluate the performance of eight state-of-the-art LLMs, including GPT-3.5, Llama-2, Falcon, and several code-specialized models, on the CSEPrompts dataset. Key findings include: LLMs perform better on programming prompts from coding websites compared to those from academic MOOCs, suggesting the academic prompts are more challenging. LLMs generally perform better at generating code than answering MCQs, despite being primarily developed for text generation. Code-specialized LLMs do not necessarily outperform general-purpose LLMs on both code generation and MCQ answering tasks. The CSEPrompts dataset provides a valuable resource for evaluating LLM capabilities in the context of computer science education, enabling researchers and educators to better understand the potential impact of these models on teaching and assessment.

Stats

The CSEPrompts dataset contains a total of 269 programming prompts and 50 multiple-choice questions. The maximum number of tokens in a prompt is 372, while the minimum is 5, with an average of 158 tokens. The MCQs have a maximum of 221 tokens, a minimum of 15, and an average of 106 tokens.

Quotes

"Recent developments in Large Language Models (LLMs), notably GPT-3 and GPT-4, have further revolutionized NLP by enabling human-like text generation and application in fields such as healthcare, education and many other novel tasks, marking a new era in generative AI." "While these models bring several opportunities in educational technology, such as enhanced writing assistants, intelligent tutoring systems, and automatic assessment tools, concerns arise from the misuse of technology, particularly in coding tasks."

Key Insights Distilled From

CSEPrompts

by Nishat Raiha... at arxiv.org 04-04-2024

https://arxiv.org/pdf/2404.02540.pdf

Deeper Inquiries

How can the CSEPrompts dataset be extended to include more advanced computer science concepts and programming tasks beyond the introductory level?

To extend the CSEPrompts dataset to cover more advanced computer science concepts and programming tasks, several strategies can be implemented: Incorporating Specialized Topics: Include prompts related to advanced topics such as data structures, algorithms, machine learning, cybersecurity, and software engineering. These prompts can challenge students with complex problem-solving tasks. Introduce Real-World Scenarios: Develop prompts that simulate real-world scenarios encountered in industry settings. This can help students apply theoretical knowledge to practical situations, enhancing their critical thinking and problem-solving skills. Integration of Advanced Programming Paradigms: Include prompts that require knowledge of advanced programming paradigms like functional programming, concurrent programming, and distributed systems. This can expose students to a wider range of programming concepts. Collaboration with Industry Professionals: Partner with industry experts to create prompts based on current trends and technologies in the field. This collaboration can ensure that the dataset remains relevant and up-to-date with industry standards. Feedback Mechanism: Implement a feedback mechanism where educators and students can suggest new prompts or provide insights on the existing ones. This can help in continuously improving the dataset and addressing any gaps in coverage. By incorporating these strategies, the CSEPrompts dataset can evolve to cater to the needs of learners at more advanced levels of computer science education.

What are the potential biases and limitations of the LLMs evaluated in this study, and how can they be addressed to improve their performance on computer science education tasks?

The LLMs evaluated in the study may have biases and limitations that can impact their performance in computer science education tasks: Bias in Training Data: LLMs can inherit biases present in the training data, leading to skewed outputs or incorrect solutions. Addressing this requires diverse and balanced training data to mitigate bias. Limited Context Understanding: LLMs may struggle with understanding context in complex programming tasks, affecting their ability to generate accurate code. Fine-tuning models on domain-specific data can enhance context comprehension. Lack of Explainability: LLMs often lack transparency in their decision-making process, making it challenging to understand how they arrive at specific solutions. Incorporating explainability features can improve trust and understanding. Overfitting to Training Data: LLMs may overfit to specific patterns in the training data, leading to poor generalization on unseen tasks. Regular evaluation on diverse datasets can help prevent overfitting. To address these biases and limitations, continuous monitoring, diverse training data, explainable AI techniques, and model evaluation on varied tasks are essential. Additionally, incorporating feedback loops from educators and students can help identify and rectify biases in the LLMs.

How can the insights from this study be leveraged to develop more effective and ethical uses of LLMs in computer science education, balancing the benefits and risks of this technology?

To leverage the insights from this study for more effective and ethical use of LLMs in computer science education, the following steps can be taken: Ethical Guidelines: Develop and adhere to ethical guidelines for using LLMs in education, ensuring transparency, fairness, and accountability in their deployment. Educator Training: Provide training to educators on how to integrate LLMs effectively into the curriculum, emphasizing the importance of critical thinking and human oversight in AI-generated content. Task-Specific Evaluation: Conduct regular evaluations of LLM performance on specific tasks to identify areas of improvement and ensure alignment with educational objectives. Student Engagement: Encourage student engagement with LLM-generated content by promoting active learning strategies and fostering a deeper understanding of the material. Risk Mitigation Strategies: Implement risk mitigation strategies to address potential misuse of LLMs, such as plagiarism detection tools, authenticity checks, and clear guidelines on acceptable use. By implementing these strategies, educators can harness the benefits of LLMs in computer science education while mitigating risks and ensuring ethical use of AI technology in the learning environment.