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ChatGPT and Open-AI Models: A Preliminary Review

Author

Listed:
  • Konstantinos I. Roumeliotis

    (Department of Informatics and Telecommunications, University of Peloponnese, 221 00 Tripoli, Greece)

  • Nikolaos D. Tselikas

    (Department of Informatics and Telecommunications, University of Peloponnese, 221 00 Tripoli, Greece)

Abstract

According to numerous reports, ChatGPT represents a significant breakthrough in the field of artificial intelligence. ChatGPT is a pre-trained AI model designed to engage in natural language conversations, utilizing sophisticated techniques from Natural Language Processing (NLP), Supervised Learning, and Reinforcement Learning to comprehend and generate text comparable to human-generated text. This article provides an overview of the training process and fundamental functionality of ChatGPT, accompanied by a preliminary review of the relevant literature. Notably, this article presents the first comprehensive literature review of this technology at the time of publication, aiming to aggregate all the available pertinent articles to facilitate further developments in the field. Ultimately, the authors aim to offer an appraisal of the technology’s potential implications on existing knowledge and technology, along with potential challenges that must be addressed.

Suggested Citation

  • Konstantinos I. Roumeliotis & Nikolaos D. Tselikas, 2023. "ChatGPT and Open-AI Models: A Preliminary Review," Future Internet, MDPI, vol. 15(6), pages 1-24, May.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:6:p:192-:d:1156389
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    References listed on IDEAS

    as
    1. Guilherme Francisco Frederico, 2023. "ChatGPT in Supply Chains: Initial Evidence of Applications and Potential Research Agenda," Logistics, MDPI, vol. 7(2), pages 1-9, April.
    2. Dowling, Michael & Lucey, Brian, 2023. "ChatGPT for (Finance) research: The Bananarama Conjecture," Finance Research Letters, Elsevier, vol. 53(C).
    3. David Rozado, 2023. "The Political Biases of ChatGPT," Social Sciences, MDPI, vol. 12(3), pages 1-8, March.
    4. Takanobu Hirosawa & Yukinori Harada & Masashi Yokose & Tetsu Sakamoto & Ren Kawamura & Taro Shimizu, 2023. "Diagnostic Accuracy of Differential-Diagnosis Lists Generated by Generative Pretrained Transformer 3 Chatbot for Clinical Vignettes with Common Chief Complaints: A Pilot Study," IJERPH, MDPI, vol. 20(4), pages 1-10, February.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Nasrabadi, Mohamadreza Azar & Beauregard, Yvan & Ekhlassi, Amir, 2024. "The implication of user-generated content in new product development process: A systematic literature review and future research agenda," Technological Forecasting and Social Change, Elsevier, vol. 206(C).
    2. Jane M. Binner & Logan J. Kelly & Jonathan A. Tepper, 2025. "Professional Forecasters vs. Shallow Neural Network Ensembles: Assessing Inflation Prediction Accuracy," JRFM, MDPI, vol. 18(4), pages 1-16, March.
    3. Liu, Yingnan & Bu, Ningbo & Li, Zhiqiang & Zhang, Yongmin & Zhao, Zhenyu, 2025. "AT-FinGPT: Financial risk prediction via an audio-text large language model," Finance Research Letters, Elsevier, vol. 77(C).
    4. Konstantinos I. Roumeliotis & Nikolaos D. Tselikas & Dimitrios K. Nasiopoulos, 2023. "Unveiling Sustainability in Ecommerce: GPT-Powered Software for Identifying Sustainable Product Features," Sustainability, MDPI, vol. 15(15), pages 1-26, August.
    5. Posedaru Bogdan-Stefan & Pantelimon Florin-Valeriu & Dulgheru Mihai-Nicolae & Georgescu Tiberiu-Marian, 2024. "Artificial Intelligence Text Processing Using Retrieval-Augmented Generation: Applications in Business and Education Fields," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 18(1), pages 209-222.
    6. Ali, Omar & Murray, Peter A. & Momin, Mujtaba & Dwivedi, Yogesh K. & Malik, Tegwen, 2024. "The effects of artificial intelligence applications in educational settings: Challenges and strategies," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    7. Stephen T. Homer, 2025. "Comparative analysis of concept mapping: human participants vs. ChatGPT," Quality & Quantity: International Journal of Methodology, Springer, vol. 59(5), pages 4873-4892, October.

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