VizTA: Enhancing Comprehension of Distributional Visualization with Visual-Lexical Fused Conversational Interface
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 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.
Liangwei Wang、Zhan Wang、Shishi Xiao、Le Liu、Fugee Tsung、Wei Zeng
信息传播、知识传播信息科学、信息技术
Liangwei Wang,Zhan Wang,Shishi Xiao,Le Liu,Fugee Tsung,Wei Zeng.VizTA: Enhancing Comprehension of Distributional Visualization with Visual-Lexical Fused Conversational Interface[EB/OL].(2025-04-20)[2025-07-20].https://arxiv.org/abs/2504.14507.点此复制
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