mlsanityData to 14 Oct 2026

A Study on the Vulnerability of Test Questions against ChatGPT-based Cheating

arXiv 2402.14881 ↗First posted 21 Feb 2024cs.CL cs.AI cs.CYcited by 0

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Abstract (arXiv)

ChatGPT is a chatbot that can answer text prompts fairly accurately, even performing very well on postgraduate-level questions. Many educators have found that their take-home or remote tests and exams are vulnerable to ChatGPT-based cheating because students may directly use answers provided by tools like ChatGPT. In this paper, we try to provide an answer to an important question: how well ChatGPT can answer test questions and how we can detect whether the questions of a test can be answered correctly by ChatGPT. We generated ChatGPT's responses to the MedMCQA dataset, which contains over 10,000 medical school entrance exam questions. We analyzed the responses and uncovered certain types of questions ChatGPT answers more inaccurately than others. In addition, we have created a basic natural language processing model to single out the most vulnerable questions to ChatGPT in a collection of questions or a sample exam. Our tool can be used by test-makers to avoid ChatGPT-vulnerable test questions.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 16 references that are in the map. First posted on arXiv 21 Feb 2024.

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References · 5

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  4. 2022MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answeringcited by 543
  5. 2023Testing the Reliability of ChatGPT for Text Annotation and Classification: A Cautionary Remarkcited by 54

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