@inproceedings{salim-etal-2024-impeding,
    title = "Impeding {LLM}-assisted Cheating in Introductory Programming Assignments via Adversarial Perturbation",
    author = "Salim, Saiful Islam  and
      Yang, Rubin Yuchan  and
      Cooper, Alexander  and
      Ray, Suryashree  and
      Debray, Saumya  and
      Rahaman, Sazzadur",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.emnlp-main.27/",
    doi = "10.18653/v1/2024.emnlp-main.27",
    pages = "445--463",
    abstract = "While Large language model (LLM)-based programming assistants such as CoPilot and ChatGPT can help improve the productivity of professional software developers, they can also facilitate cheating in introductory computer programming courses. Assuming instructors have limited control over the industrial-strength models, this paper investigates the baseline performance of 5 widely used LLMs on a collection of introductory programming problems, examines adversarial perturbations to degrade their performance, and describes the results of a user study aimed at measuring the efficacy of such perturbations in hindering actual code generation for introductory programming assignments. The user study suggests that i) perturbations combinedly reduced the average correctness score by 77{\%}, ii) the drop in correctness caused by these perturbations was affected based on their detectability."
}