Konferenzbeitrag
From ChatGPT to FactGPT: A Participatory Design Study to Mitigate the Effects of Large Language Model Hallucinations on Users
Vorschaubild nicht verfügbar
Volltext URI
Dokumententyp
Text/Conference Paper
Zusatzinformation
Datum
2023
Zeitschriftentitel
ISSN der Zeitschrift
Bandtitel
Verlag
ACM
Zusammenfassung
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.