Review of Detecting Text generated by ChatGPT Using Machine and Deep-Learning Models: A Tools and Methods Analysis
Recently, generative models, such as ChatGPT, have gained considerable attention because of their capacity to generate text almost identical to that produced by humans. However, ChatGPT raises several concerns, particularly regarding the integrity of academic work, the protection of personal inform...
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University of Diyala
2025-03-01
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| Series: | Diyala Journal of Engineering Sciences |
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| Online Access: | https://djes.info/index.php/djes/article/view/1676 |
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| author | Shaymaa Dhyaa Aldeen Thekra Abbas Ayad Rodhan Abbas |
| author_facet | Shaymaa Dhyaa Aldeen Thekra Abbas Ayad Rodhan Abbas |
| author_sort | Shaymaa Dhyaa Aldeen |
| collection | DOAJ |
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Recently, generative models, such as ChatGPT, have gained considerable attention because of their capacity to generate text almost identical to that produced by humans. However, ChatGPT raises several concerns, particularly regarding the integrity of academic work, the protection of personal information and security, the reliance on artificial intelligence (AI), the evaluation of learning, and the precision of information. Distinguishing between writing generated by machines and text that humans wrote is one of the most critical issues at present. The purpose of this literature review is to provide a comprehensive, up-to-date analysis of the most recent methods for identifying text that ChatGPT created. It examines more than 60 academic papers, especially research articles published after the model’s release in 2022, and analyzes state-of-the-art machine learning, deep learning, and hybrid approaches for detecting AI-generated text. The review categorizes detection methods into statistical models, transformer-based architectures, perplexity-based techniques, and human-assisted evaluation. The findings indicate that deep learning models, particularly the Robustly Optimized BERT Pretraining Approach (RoBERTa) and Cross-lingual Language Model with RoBERTa Architecture, have high detection accuracy (up to 99%), whereas traditional statistical methods exhibit limitations in distinguishing complex AI-generated content. This work recommends the use of machine and deep learning techniques and human reviewers in ongoing efforts to distinguish between AI-generated and human-written text. However, given the increasing sophistication and complexity of models, such as ChatGPT, detection techniques have to be continuously improved and innovated to ensure reliability and maintain the integrity of content across various sectors.
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| format | Article |
| id | doaj-art-bfad5597c3404dec9c0ddb7c6cfd7398 |
| institution | DOAJ |
| issn | 1999-8716 2616-6909 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | University of Diyala |
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| series | Diyala Journal of Engineering Sciences |
| spelling | doaj-art-bfad5597c3404dec9c0ddb7c6cfd73982025-08-20T03:14:45ZengUniversity of DiyalaDiyala Journal of Engineering Sciences1999-87162616-69092025-03-0118110.24237/djes.2025.18102Review of Detecting Text generated by ChatGPT Using Machine and Deep-Learning Models: A Tools and Methods AnalysisShaymaa Dhyaa Aldeen0 Thekra Abbas1 Ayad Rodhan Abbas2Department of Computer Science, College of Science, Mustansiriyah University, Baghdad, Iraq.Department of Computer Science, College of Science, Mustansiriyah University, Baghdad, Iraq.Department of Computer Sciences, University of Technology, Baghdad, Iraq. Recently, generative models, such as ChatGPT, have gained considerable attention because of their capacity to generate text almost identical to that produced by humans. However, ChatGPT raises several concerns, particularly regarding the integrity of academic work, the protection of personal information and security, the reliance on artificial intelligence (AI), the evaluation of learning, and the precision of information. Distinguishing between writing generated by machines and text that humans wrote is one of the most critical issues at present. The purpose of this literature review is to provide a comprehensive, up-to-date analysis of the most recent methods for identifying text that ChatGPT created. It examines more than 60 academic papers, especially research articles published after the model’s release in 2022, and analyzes state-of-the-art machine learning, deep learning, and hybrid approaches for detecting AI-generated text. The review categorizes detection methods into statistical models, transformer-based architectures, perplexity-based techniques, and human-assisted evaluation. The findings indicate that deep learning models, particularly the Robustly Optimized BERT Pretraining Approach (RoBERTa) and Cross-lingual Language Model with RoBERTa Architecture, have high detection accuracy (up to 99%), whereas traditional statistical methods exhibit limitations in distinguishing complex AI-generated content. This work recommends the use of machine and deep learning techniques and human reviewers in ongoing efforts to distinguish between AI-generated and human-written text. However, given the increasing sophistication and complexity of models, such as ChatGPT, detection techniques have to be continuously improved and innovated to ensure reliability and maintain the integrity of content across various sectors. https://djes.info/index.php/djes/article/view/1676Text DetectionMachine learningDeep LearningNLPTransform CodingChatGPT |
| spellingShingle | Shaymaa Dhyaa Aldeen Thekra Abbas Ayad Rodhan Abbas Review of Detecting Text generated by ChatGPT Using Machine and Deep-Learning Models: A Tools and Methods Analysis Diyala Journal of Engineering Sciences Text Detection Machine learning Deep Learning NLP Transform Coding ChatGPT |
| title | Review of Detecting Text generated by ChatGPT Using Machine and Deep-Learning Models: A Tools and Methods Analysis |
| title_full | Review of Detecting Text generated by ChatGPT Using Machine and Deep-Learning Models: A Tools and Methods Analysis |
| title_fullStr | Review of Detecting Text generated by ChatGPT Using Machine and Deep-Learning Models: A Tools and Methods Analysis |
| title_full_unstemmed | Review of Detecting Text generated by ChatGPT Using Machine and Deep-Learning Models: A Tools and Methods Analysis |
| title_short | Review of Detecting Text generated by ChatGPT Using Machine and Deep-Learning Models: A Tools and Methods Analysis |
| title_sort | review of detecting text generated by chatgpt using machine and deep learning models a tools and methods analysis |
| topic | Text Detection Machine learning Deep Learning NLP Transform Coding ChatGPT |
| url | https://djes.info/index.php/djes/article/view/1676 |
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