A Pilot Study Comparing ChatGPT and Google Search in Supporting Visualization Insight Discovery

Chen He, Robin Welsch, Giulio Jacucci

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussaKonferenssiartikkeliTieteellinenvertaisarvioitu


The popularity of large language models (LLMs) provides new possibilities for deriving visualization insights, integrating human and machine intelligence. However, we have yet to understand how a contextualized LLM compares with the traditional search in supporting visualization insight discovery. To this end, we conducted a between-subjects study with 25 participants to compare user insight generation with chat/search on a CO2 Explorer. The Chat condition has ChatGPT contextualized with the data, user tasks, and interactions as programmed system prompts. Results show both systems have their merits and demerits: ChatGPT affords users to ask more diverse questions but can produce wrong answers; Search provides information sources, making the answer more reliable, but users can fail to find the answer. This study prompts us to synthesize them in a future study for reliable and efficient information retrieval.

OtsikkoJoint Proceedings of the ACM IUI Workshops 2024, March 18-21, 2024, Greenville, South Carolina, USA
ToimittajatAxel Soto, Eva Zangerle
TilaJulkaistu - 2024
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaWorkshops at the International Conference on Intelligent User Interfaces - Greenville, Yhdysvallat (USA)
Kesto: 18 maalisk. 202418 maalisk. 2024


NimiCEUR Workshop Proceedings
ISSN (painettu)1613-0073


Publisher Copyright:
© 2024 Copyright for this paper by its authors.


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