IDEAS home Printed from https://ideas.repec.org/a/pal/palcom/v11y2024i1d10.1057_s41599-024-03811-x.html
   My bibliography  Save this article

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

Author

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," Palgrave 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41599-024-03811-x
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1057/s41599-024-03811-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Željana Bašić & Ana Banovac & Ivana Kružić & Ivan Jerković, 2023. "ChatGPT-3.5 as writing assistance in students’ essays," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-5, December.
    2. 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.
    3. Marius Dragomir & José Rúas-Araújo & Minna Horowitz, 2024. "Beyond online disinformation: assessing national information resilience in four European countries," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-10, December.
    4. 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," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-12, December.
    5. Athanasios Polyportis & Nikolaos Pahos, 2024. "Navigating the perils of artificial intelligence: a focused review on ChatGPT and responsible research and innovation," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-10, December.
    6. Minhyeok Lee, 2023. "A Mathematical Investigation of Hallucination and Creativity in GPT Models," Mathematics, MDPI, vol. 11(10), pages 1-17, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Meng Wang & Yalin Qin & Jiaojiao Liu & Weidong Li, 2023. "Identifying personal physiological data risks to the Internet of Everything: the case of facial data breach risks," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-15, December.
    2. Janik Ole Wecks & Johannes Voshaar & Benedikt Jost Plate & Jochen Zimmermann, 2024. "Generative AI Usage and Exam Performance," Papers 2404.19699, arXiv.org, revised Nov 2024.
    3. Divine Q. Agozie & Muesser Nat, 2022. "Do communication content functions drive engagement among interest group audiences? An analysis of organizational communication on Twitter," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-9, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:palcom:v:11:y:2024:i:1:d:10.1057_s41599-024-03811-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: https://www.nature.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.