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A Bibliometric Review of Large Language Model Hallucination

Author

Listed:
  • Nur Emma Mustaffa

    (Department of Quantity Surveying, Faculty of Built Environment, Tunku Abdul Rahman University of Management and Technology, Kuala Lumpur, Malaysia)

  • Ke En Lai

    (Department of Quantity Surveying, Faculty of Built Environment, Tunku Abdul Rahman University of Management and Technology, Kuala Lumpur, Malaysia)

  • Christopher Nigel Preece

    (Department of Construction Management, Faculty of Built Environment, Tunku Abdul Rahman University of Management and Technology, Kuala Lumpur, Malaysia)

  • Foo Yeu Wong

    (Department of Quantity Surveying, Faculty of Built Environment, Tunku Abdul Rahman University of Management and Technology, Kuala Lumpur, Malaysia)

Abstract

Hallucination, defined as plausible but factually incorrect outputs generated by Large Language Models, poses risks to knowledge reliability. Despite the growing use of AI in higher education, most research has concentrated on healthcare, leaving academic practices underexplored. This paper reviews research on hallucination in LLMs, with a focus on its implications for academia. While AI-generated content is increasingly used in education and research, limited attention has been given to how hallucinations affect academic credibility and integrity. A bibliometric analysis was conducted using Scopus, retrieving and analysing a total of 2,491 documents with VOSviewer. Keyword co-occurrence mapping, supported by a thesaurus file, was used to identify research trends and thematic clusters. This study provides a structured overview of hallucination-related research, highlights underexplored domains such as education and non-healthcare industriessss, and identifies priorities for future research. Hallucination is a central concern in LLM research, yet discussion is concentrated in healthcare. Academic contexts and technical fields like construction and law remain under-investigated, indicating a significant gap in awareness and application. Educators should integrate AI literacy and hallucination awareness into academic integrity training. Future studies should examine domain-specific causes and impacts of hallucination in academia and beyond healthcare. Raising awareness of LLM hallucinations can safeguard knowledge integrity, reduce misinformation, and promote ethical AI adoption. Further studies are needed in education, law, and industry-specific settings, alongside the development of robust detection and mitigation strategies.

Suggested Citation

  • Nur Emma Mustaffa & Ke En Lai & Christopher Nigel Preece & Foo Yeu Wong, 2025. "A Bibliometric Review of Large Language Model Hallucination," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 9(9), pages 5025-5037, September.
    • Handle: RePEc:bcp:journl:v:9:y:2025:issue-9:p:5025-5037
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    References listed on IDEAS

    as
    1. Brady D. Lund & Ting Wang & Nishith Reddy Mannuru & Bing Nie & Somipam Shimray & Ziang Wang, 2023. "ChatGPT and a new academic reality: Artificial Intelligence‐written research papers and the ethics of the large language models in scholarly publishing," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(5), pages 570-581, May.
    2. Nees Jan Eck & Ludo Waltman, 2010. "Software survey: VOSviewer, a computer program for bibliometric mapping," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(2), pages 523-538, August.
    3. Waltman, Ludo & van Eck, Nees Jan & Noyons, Ed C.M., 2010. "A unified approach to mapping and clustering of bibliometric networks," Journal of Informetrics, Elsevier, vol. 4(4), pages 629-635.
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