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Distinguishing Human Generated Text From ChatGPT Generated Text Using Machine Learning

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arxiv 2306.01761 v1 pith:2UKDOZ2C submitted 2023-05-26 cs.CL cs.AIcs.LG

Niful Islam , Debopom Sutradhar , Humaira Noor , Jarin Tasnim Raya , Monowara Tabassum Maisha , Dewan Md Farid This is my paper · ORCID
classification cs.CLcs.AIcs.LG
keywords textlearningmodelchatgptgeneratedgenerativemachinewritten
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ChatGPT is a conversational artificial intelligence that is a member of the generative pre-trained transformer of the large language model family. This text generative model was fine-tuned by both supervised learning and reinforcement learning so that it can produce text documents that seem to be written by natural intelligence. Although there are numerous advantages of this generative model, it comes with some reasonable concerns as well. This paper presents a machine learning-based solution that can identify the ChatGPT delivered text from the human written text along with the comparative analysis of a total of 11 machine learning and deep learning algorithms in the classification process. We have tested the proposed model on a Kaggle dataset consisting of 10,000 texts out of which 5,204 texts were written by humans and collected from news and social media. On the corpus generated by GPT-3.5, the proposed algorithm presents an accuracy of 77%.

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Cited by 2 Pith papers

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  1. Detecting AI-Generated Text in Educational Content: Leveraging Machine Learning and Explainable AI for Academic Integrity

    cs.CL 2025-01 conditional novelty 4.0 of 10

    XGBoost and Random Forest can distinguish ChatGPT-written cybersecurity paragraphs from human Wikipedia paragraphs with 81 to 83% accuracy, and a narrow XGBoost model beat GPTZero in a three-class test, yet the benchm...

  2. Using Machine Learning to Distinguish Human-written from Machine-generated Creative Fiction

    cs.CL 2024-12 conditional novelty 4.0 of 10

    Naive Bayes and MLP classifiers distinguish about 100-word excerpts of human-written detective fiction from ChatGPT-3.5 output with roughly 96% accuracy on in-domain tests.

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