VizTA: Enhancing Comprehension of Distributional Visualization with Visual‐Lexical Fused Conversational Interface

Comprehending visualizations requires readers to interpret visual encoding and the underlying meanings actively. This poses challenges for visualization novices, particularly when interpreting distributional visualizations that depict statistical uncertainty. Advancements in LLM‐based conversational...

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Vydané v:Computer graphics forum Ročník 44; číslo 3
Hlavní autori: Wang, Liangwei, Wang, Zhan, Xiao, Shishi, Liu, Le, Tsung, Fugee, Zeng, Wei
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford Blackwell Publishing Ltd 01.06.2025
Predmet:
CCS Concepts
Charts
Feedback
Human‐centered computing → Visualization systems and tools
Visualization
ISSN:0167-7055, 1467-8659
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Shrnutí:Comprehending visualizations requires readers to interpret visual encoding and the underlying meanings actively. This poses challenges for visualization novices, particularly when interpreting distributional visualizations that depict statistical uncertainty. Advancements in LLM‐based conversational interfaces show promise in promoting visualization comprehension. However, they fail to provide contextual explanations at fine‐grained granularity, and chart readers are still required to mentally bridge visual information and textual explanations during conversations. Our formative study highlights the expectations for both lexical and visual feedback, as well as the importance of explicitly linking these two modalities throughout the conversation. The findings motivate the design of VizTA, a visualization teaching assistant that leverages the fusion of visual and lexical feedback to help readers better comprehend visualization. VizTA features a semantic‐aware conversational agent capable of explaining contextual information within visualizations and employs a visual‐lexical fusion design to facilitate chart‐centered conversation. A between‐subject study with 24 participants demonstrates the effectiveness of VizTA in supporting the understanding and reasoning tasks of distributional visualization across multiple scenarios.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.70110