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AI hallucination: towards a comprehensive classification of distorted information in artificial intelligence-generated content

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Listed:
  • Yujie Sun

    (Shandong Normal University Library)

  • Dongfang Sheng

    (Shandong University)

  • Zihan Zhou

    (Shandong University)

  • Yifei Wu

    (Shandong University)

Abstract

Amidst the burgeoning information age, the rapid development of artificial intelligence-generated content (AIGC) has brought forth challenges regarding information authenticity. The proliferation of distorted information significantly impacts users negatively. This study aims to systematically categorize distorted information within AIGC, delve into its internal characteristics, and provide theoretical guidance for its management. Utilizing ChatGPT as a case study, we conducted empirical content analysis on 243 instances of distorted information collected, comprising both questions and answers. Three coders meticulously interpreted each instance of distorted information, encoding error points based on a predefined coding scheme and categorizing them according to error type. Our objective was to refine and validate the distorted information category list derived from the review through multiple rounds of pre-coding and test coding, thereby yielding a comprehensive and clearly delineated category list of distorted information in AIGC. The findings identified 8 first-level error types: “Overfitting”; “Logic errors”; “Reasoning errors”; “Mathematical errors”; “Unfounded fabrication”; “Factual errors”; “Text output errors”; and “Other errors”, further subdivided into 31 second-level error types. This classification list not only lays a solid foundation for studying risks associated with AIGC but also holds significant practical implications for helping users identify distorted information and enabling developers to enhance the quality of AI-generated tools.

Suggested Citation

  • Yujie Sun & Dongfang Sheng & Zihan Zhou & Yifei Wu, 2024. "AI hallucination: towards a comprehensive classification of distorted information in artificial intelligence-generated content," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 11(1), pages 1-14, December.
    • Handle: RePEc:pal:palcom:v:11:y:2024:i:1:d:10.1057_s41599-024-03811-x
    DOI: 10.1057/s41599-024-03811-x
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    References listed on IDEAS

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    1. Marius Dragomir & José Rúas-Araújo & Minna Horowitz, 2024. "Beyond online disinformation: assessing national information resilience in four European countries," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 11(1), pages 1-10, December.
    2. Nadejda Komendantova & Love Ekenberg & Mattias Svahn & Aron Larsson & Syed Iftikhar Hussain Shah & Myrsini Glinos & Vasilis Koulolias & Mats Danielson, 2021. "A value-driven approach to addressing misinformation in social media," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 8(1), pages 1-12, December.
    3. Athanasios Polyportis & Nikolaos Pahos, 2024. "Navigating the perils of artificial intelligence: a focused review on ChatGPT and responsible research and innovation," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 11(1), pages 1-10, December.
    4. Željana Bašić & Ana Banovac & Ivana Kružić & Ivan Jerković, 2023. "ChatGPT-3.5 as writing assistance in students’ essays," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 10(1), pages 1-5, December.
    5. Ionuț-Daniel Anastasiei & Mircea Radu Georgescu, 2020. "Automated vs Manual Content Analysis – A Retrospective Look," Scientific Annals of Economics and Business (continues Analele Stiintifice), Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 67(4), pages 57-67, December.
    6. Minhyeok Lee, 2023. "A Mathematical Investigation of Hallucination and Creativity in GPT Models," Mathematics, MDPI, vol. 11(10), pages 1-17, May.
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    Cited by:

    1. Sarah V. Bentley, 2025. "Knowing you know nothing in the age of generative AI," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-7, December.
    2. Ivanov, Dmitry, 2025. "Conceptual and formal models for design, adaptation, and control of digital twins in supply chain ecosystems," Omega, Elsevier, vol. 137(C).

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