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Contribution Details
| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | Yes |
| Title | From ChatGPT to FactGPT: A Participatory Design Study to Mitigate the Effects of Large Language Model Hallucinations on Users |
| Organization Unit | |
| Authors |
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| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | No |
| Status | Published in final form |
| Language |
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| ISBN | 979-8-4007-0771-1 |
| Page Range | 81 - 90 |
| Event Title | MuC '23: Mensch und Computer 2023 |
| Event Type | conference |
| Event Location | Rapperswil Switzerland |
| Event Start Date | September 3 - 2023 |
| Event End Date | September 6 - 2023 |
| Series Name | Mensch und Computer Conference Proceedings |
| Publisher | ACM Digital library |
| Abstract Text | 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. |
| Digital Object Identifier | 10.1145/3603555.3603565 |
| Other Identification Number | merlin-id:24361 |
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