Distinguishing Human Generated Text From ChatGPT Generated Text Using Machine Learning
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Abstract (arXiv)
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%.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 16 of its 27 references that are in the map. First posted on arXiv 26 May 2023.
Builds on
16 of 27 references.
References · 16
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4 more
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Built on by
Citing papers per year: 2023: 2; 2024: 7; 2025: 0; 2026: 1.
10 papers.
- 2024SaulLM-7B: A pioneering Large Language Model for Lawcited by 142
- 2026GPTZero: Robust Detection of LLM-Generated Textscited by 11
- 2024Topic-Based Watermarks for Large Language Modelscited by 8
- 2024ChatGPT Role-play Dataset: Analysis of User Motives and Model Naturalnesscited by 7
- 2024ChatGPT Code Detection: Techniques for Uncovering the Source of Codecited by 6
- 2024Keystroke Dynamics Against Academic Dishonesty in the Age of LLMscited by 6
- 2023AuthentiGPT: Detecting Machine-Generated Text via Black-Box Language Models Denoisingcited by 5
- 2024Using Machine Learning to Distinguish Human-written from Machine-generated Creative Fictioncited by 4
- 2023EWasteNet: A Two-Stream Data Efficient Image Transformer Approach for E-Waste Classificationcited by 2
- 2024SMLT-MUGC: Small, Medium, and Large Texts -- Machine versus User-Generated Content Detection and Comparisoncited by 0
- 2026GPTZero: Robust Detection of LLM-Generated Texts13 Feb 2026
- 2024Using Machine Learning to Distinguish Human-written from Machine-generated Creative Fiction15 Dec 2024
- 2024SMLT-MUGC: Small, Medium, and Large Texts -- Machine versus User-Generated Content Detection and Comparison28 Jun 2024
- 2024Keystroke Dynamics Against Academic Dishonesty in the Age of LLMs21 Jun 2024
- 2024ChatGPT Code Detection: Techniques for Uncovering the Source of Code24 May 2024
- 2024Topic-Based Watermarks for Large Language Models2 Apr 2024
- 2024ChatGPT Role-play Dataset: Analysis of User Motives and Model Naturalness26 Mar 2024
- 2024SaulLM-7B: A pioneering Large Language Model for Law6 Mar 2024
- 2023AuthentiGPT: Detecting Machine-Generated Text via Black-Box Language Models Denoising13 Nov 2023
- 2023EWasteNet: A Two-Stream Data Efficient Image Transformer Approach for E-Waste Classification28 Sep 2023