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Striking the Balance in Using LLMs for Fact-Checking: A Narrative Literature Review

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Disinformation in Open Online Media (MISDOOM 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15175))

Abstract

The launch of ChatGPT at the end of November 2022 triggered a general reflection on its benefits for supporting fact-checking workflows and practices. Between the excitement of the availability of AI systems that no longer require the mastery of programming skills and the exploration of a new field of experimentation, academics and professionals foresaw the benefits of such technology. Critics have raised concerns about the fairness and of the data used to train Large Language Models (LLMs), including the risk of artificial hallucinations and the proliferation of machine-generated content that could spread misinformation. As LLMs pose ethical challenges, how can professional fact-checking mitigate risks? This narrative literature review explores the current state of LLMs in the context of fact-checking practice, highlighting three key complementary mitigation strategies related to education, ethics and professional practice.

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Acknowledgments

This research was funded by EU CEF Grant No. 101158604.

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Authors and Affiliations

  1. Department of Information Science and Media Studies, University of Bergen, Bergen, Norway

    Laurence Dierickx, Andreas L. Opdahl & Carl-Gustav Lindén

  2. Digital Democracy Centre, University of Southern Denmark, Odense, Denmark

    Laurence Dierickx & Arjen van Dalen

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  1. Laurence Dierickx
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  2. Arjen van Dalen
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Correspondence to Laurence Dierickx .

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  1. Leiden University, Leiden, The Netherlands

    Mike Preuss

  2. University of Twente, Enschede, The Netherlands

    Agata Leszkiewicz

  3. University of Calgary, Calgary, AB, Canada

    Jean-Christopher Boucher

  4. King's College London, London, UK

    Ofer Fridman

  5. University of Münster, Münster, Germany

    Lucas Stampe

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Dierickx, L., van Dalen, A., Opdahl, A.L., Lindén, CG. (2024). Striking the Balance in Using LLMs for Fact-Checking: A Narrative Literature Review. In: Preuss, M., Leszkiewicz, A., Boucher, JC., Fridman, O., Stampe, L. (eds) Disinformation in Open Online Media. MISDOOM 2024. Lecture Notes in Computer Science, vol 15175. Springer, Cham. https://doi.org/10.1007/978-3-031-71210-4_1

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