1 citations · 3 across the 11 of their papers we have counts for
8 papers · 1 filter
ThinkMorph: Emergent Properties in Multimodal Interleaved Chain-of-Thought Reasoning
Jiawei Gu, Yunzhuo Hao, Huichen Will Wang +5
Multimodal reasoning requires iterative coordination between language and vision, yet it remains unclear what constitutes a meaningful interleaved chain of thought. We posit that t…
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,…
FullFront: Benchmarking MLLMs Across the Full Front-End Engineering Workflow
Haoyu Sun, Huichen Will Wang, Jiawei Gu +2
Front-end engineering involves a complex workflow where engineers conceptualize designs, translate them into code, and iteratively refine the implementation. While recent benchmark…
Lost in Benchmarks? Rethinking Large Language Model Benchmarking with Item Response Theory
Hongli Zhou, Hui Huang, Ziqing Zhao +10
The evaluation of large language models (LLMs) via benchmarks is widespread, yet inconsistencies between different leaderboards and poor separability among top models raise concern…
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…
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…
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…
Can MLLMs Reason in Multimodality? EMMA: An Enhanced MultiModal ReAsoning Benchmark
Yunzhuo Hao, Jiawei Gu, Huichen Will Wang +4
The ability to organically reason over and with both text and images is a pillar of human intelligence, yet the ability of Multimodal Large Language Models (MLLMs) to perform such…