How Beginning Programmers and Code LLMs (Mis)read Each Other

Author:

Nguyen Sydney1ORCID,Babe Hannah McLean2ORCID,Zi Yangtian3ORCID,Guha Arjun4ORCID,Anderson Carolyn Jane1ORCID,Feldman Molly Q2ORCID

Affiliation:

1. Department of Computer Science, Wellesley College, United States

2. Department of Computer Science, Oberlin College, United States

3. Northeastern University, United States

4. Computer Science, Northeastern University, United States and Research, Roblox, United States

Funder

National Science Foundation

Publisher

ACM

Reference104 articles.

1. Andrea Agostinelli Timo I. Denk Zalán Borsos Jesse Engel Mauro Verzetti Antoine Caillon Qingqing Huang Aren Jansen Adam Roberts Marco Tagliasacchi Matt Sharifi Neil Zeghidour and Christian Frank. 2023. MusicLM: Generating Music From Text. http://arxiv.org/abs/2301.11325

2. Nader Akoury, Shufan Wang, Josh Whiting, Stephen Hood, Nanyun Peng, and Mohit Iyyer. 2020. STORIUM: A Dataset and Evaluation Platform for Machine-in-the-Loop Story Generation. http://arxiv.org/abs/2010.01717 arXiv:2010.01717 [cs].

3. Hannah McLean Babe Sydney Nguyen Yangtian Zi Arjun Guha Molly Q. Feldman and Carolyn Jane Anderson. 2023. StudentEval: A Benchmark of Student-Written Prompts for Large Language Models of Code. http://arxiv.org/abs/2306.04556

4. Programming in natural language

5. Patrick Bareiß Beatriz Souza Marcelo d’Amorim and Michael Pradel. 2022. Code Generation Tools (Almost) for Free? A Study of Few-Shot Pre-Trained Language Models on Code. http://arxiv.org/abs/2206.01335 arXiv:2206.01335 [cs].

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1. Non-Expert Programmers in the Generative AI Future;Proceedings of the 3rd Annual Meeting of the Symposium on Human-Computer Interaction for Work;2024-06-25

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