Neural Language Models are Effective Plagiarists

• URL: http://arxiv.org/abs/2201.07406v1
• Date: Wed, 19 Jan 2022 04:00:46 GMT
• Title: Neural Language Models are Effective Plagiarists
• Authors: Stella Biderman and Edward Raff
• Abstract summary: We find that a student using GPT-J can complete introductory level programming assignments without triggering suspicion from MOSS. GPT-J was not trained on the problems in question and is not provided with any examples to work from. We conclude that the code written by GPT-J is diverse in structure, lacking any particular tells that future plagiarism detection techniques may use to try to identify algorithmically generated code.
• Score: 38.85940137464184
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As artificial intelligence (AI) technologies become increasingly powerful and prominent in society, their misuse is a growing concern. In educational settings, AI technologies could be used by students to cheat on assignments and exams. In this paper we explore whether transformers can be used to solve introductory level programming assignments while bypassing commonly used AI tools to detect plagiarism. We find that a student using GPT-J [Wang and Komatsuzaki, 2021] can complete introductory level programming assignments without triggering suspicion from MOSS [Aiken, 2000], a widely used plagiarism detection tool. This holds despite the fact that GPT-J was not trained on the problems in question and is not provided with any examples to work from. We further find that the code written by GPT-J is diverse in structure, lacking any particular tells that future plagiarism detection techniques may use to try to identify algorithmically generated code. We conclude with a discussion of the ethical and educational implications of large language models and directions for future research.

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