research-article

From ChatGPT to FactGPT: A Participatory Design Study to Mitigate the Effects of Large Language Model Hallucinations on Users

Authors:

Florian Leiser,

Merlin Knaeble,

Alexander Maedche,

Gerhard Schwabe,

Ali SunyaevAuthors Info & Claims

MuC '23: Proceedings of Mensch und Computer 2023

Pages 81 - 90

https://doi.org/10.1145/3603555.3603565

Published: 03 September 2023 Publication History

Abstract

Large language models (LLMs) like ChatGPT recently gained interest across all walks of life with their human-like quality in textual responses. Despite their success in research, healthcare, or education, LLMs frequently include incorrect information, called hallucinations, in their responses. These hallucinations could influence users to trust fake news or change their general beliefs. Therefore, we investigate mitigation strategies desired by users to enable identification of LLM hallucinations. To achieve this goal, we conduct a participatory design study where everyday users design interface features which are then assessed for their feasibility by machine learning (ML) experts. We find that many of the desired features are well-perceived by ML experts but are also considered as difficult to implement. Finally, we provide a list of desired features that should serve as a basis for mitigating the effect of LLM hallucinations on users.

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Cited By

Humburg MDragnić-Cindrić DHmelo-Silver CGlazewski KLester JDanish J(2024)Integrating Youth Perspectives into the Design of AI-Supported Collaborative Learning EnvironmentsEducation Sciences10.3390/educsci1411119714:11(1197)Online publication date: 31-Oct-2024
https://doi.org/10.3390/educsci14111197
Vahid F(2024)AI in CS Education: Opportunities, Challenges, and Pitfalls to AvoidACM Inroads10.1145/367920515:3(52-57)Online publication date: 21-Aug-2024
https://dl.acm.org/doi/10.1145/3679205
Lee YSon KKim TKim JChung JAdar EKim J(2024)One vs. Many: Comprehending Accurate Information from Multiple Erroneous and Inconsistent AI GenerationsProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3662681(2518-2531)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3662681
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Index Terms

From ChatGPT to FactGPT: A Participatory Design Study to Mitigate the Effects of Large Language Model Hallucinations on Users
1. Computing methodologies
  1. Artificial intelligence
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
    2. Interaction techniques
  2. Visualization
    1. Visualization design and evaluation methods

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MuC '23: Proceedings of Mensch und Computer 2023

September 2023

593 pages

ISBN:9798400707711

DOI:10.1145/3603555

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Published: 03 September 2023

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MuC '23: Mensch und Computer 2023

September 3 - 6, 2023

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Cited By

Humburg MDragnić-Cindrić DHmelo-Silver CGlazewski KLester JDanish J(2024)Integrating Youth Perspectives into the Design of AI-Supported Collaborative Learning EnvironmentsEducation Sciences10.3390/educsci1411119714:11(1197)Online publication date: 31-Oct-2024
https://doi.org/10.3390/educsci14111197
Vahid F(2024)AI in CS Education: Opportunities, Challenges, and Pitfalls to AvoidACM Inroads10.1145/367920515:3(52-57)Online publication date: 21-Aug-2024
https://dl.acm.org/doi/10.1145/3679205
Lee YSon KKim TKim JChung JAdar EKim J(2024)One vs. Many: Comprehending Accurate Information from Multiple Erroneous and Inconsistent AI GenerationsProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3662681(2518-2531)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3662681
Sun XLiu YDe Wit JBosch JLi Z(2024)Trust by Interface: How Different User Interfaces Shape Human Trust in Health Information from Large Language ModelsExtended Abstracts of the CHI Conference on Human Factors in Computing Systems10.1145/3613905.3650837(1-7)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613905.3650837
Leiser FEckhardt SLeuthe VKnaeble MMädche ASchwabe GSunyaev A(2024)HILL: A Hallucination Identifier for Large Language ModelsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642428(1-13)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642428
Rogers KWebber RGorostiaga Zubizarreta GMelo Cruz AChen SArkin RBorenstein JWagner AGrollman DBroadbent EJu WSoh HWilliams T(2024)What Should a Robot Do? Comparing Human and Large Language Model Recommendations for Robot DeceptionCompanion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction10.1145/3610978.3640752(906-910)Online publication date: 11-Mar-2024
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Yan LGreiff STeuber ZGašević D(2024)Promises and challenges of generative artificial intelligence for human learningNature Human Behaviour10.1038/s41562-024-02004-58:10(1839-1850)Online publication date: 22-Oct-2024
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Maya FWolf K(2024)An Architecture for Formative Assessment Analytics of Multimodal Artefacts in ePortfolios Supported by Artificial IntelligenceAssessment Analytics in Education10.1007/978-3-031-56365-2_15(293-312)Online publication date: 8-May-2024
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