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20242026
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cs.HC2025

Do You "Trust" This Visualization? An Inventory to Measure Trust in Visualizations

Huichen Will Wang, Kylie Lin, Andrew Cohen +4

Trust plays a critical role in visual data communication and decision-making, yet existing visualization research employs varied trust measures, making it challenging to compare an…

cs.HC2025

Tell Me Without Telling Me: Two-Way Prediction of Visualization Literacy and Visual Attention

Minsuk Chang, Yao Wang, Huichen Will Wang +3

Accounting for individual differences can improve the effectiveness of visualization design. While the role of visual attention in visualization interpretation is well recognized,…

cs.HC2025

Grid Labeling: Crowdsourcing Task-Specific Importance from Visualizations

Minsuk Chang, Yao Wang, Huichen Will Wang +2

Knowing where people look in visualizations is key to effective design. Yet, existing research primarily focuses on free-viewing-based saliency models - although visual attention i…

cs.HC2025

Jupybara: Operationalizing a Design Space for Actionable Data Analysis and Storytelling with LLMs

Huichen Will Wang, Larry Birnbaum, Vidya Setlur

Mining and conveying actionable insights from complex data is a key challenge of exploratory data analysis (EDA) and storytelling. To address this challenge, we present a design sp…

cs.HC2024

DracoGPT: Extracting Visualization Design Preferences from Large Language Models

Huichen Will Wang, Mitchell Gordon, Leilani Battle +1

Trained on vast corpora, Large Language Models (LLMs) have the potential to encode visualization design knowledge and best practices. However, if they fail to do so, they might pro…

cs.HC2024

How Aligned are Human Chart Takeaways and LLM Predictions? A Case Study on Bar Charts with Varying Layouts

Huichen Will Wang, Jane Hoffswell, Sao Myat Thazin Thane +2

Large Language Models (LLMs) have been adopted for a variety of visualizations tasks, but how far are we from perceptually aware LLMs that can predict human takeaways? Graphical pe…