90 citations · 92 across the 4 of their papers we have counts for
5 papers
ChartAnno: Benchmarking Multimodal Large Language Models for Chart Annotation Generation
Zhenghan Chen, Zekai Shao, Lidan Tan +10
Annotations are essential to communicative visualization, helping explain data, emphasize key findings, and guide attention. While multimodal large language models (MLLMs) offer ne…
Semantic-Structural Alignment for Generative Pictorial Charts
Zhida Sun, Yulin Zhang, Zheng Gu +4
Traditional statistical graphics are precise but often lack the visual appeal, memorability, and engagement of pictorial charts. We present a generative framework for the automated…
: Scalable Auto-Feedback for LLM-based Chart Generation
Woosung Koh, Jang Han Yoon, MinHyung Lee +7
Generating high-quality charts with Large Language Models (LLMs) presents significant challenges due to limited data and the high cost of scaling through human curation. $\langle \…
Data Formulator 2: Iterative Creation of Data Visualizations, with AI Transforming Data Along the Way
Chenglong Wang, Bongshin Lee, Steven Drucker +2
Data analysts often need to iterate between data transformations and chart designs to create rich visualizations for exploratory data analysis. Although many AI-powered systems hav…
Data Visceralization: Enabling Deeper Understanding of Data Using Virtual Reality
Benjamin Lee, Dave Brown, Bongshin Lee +3
A fundamental part of data visualization is transforming data to map abstract information onto visual attributes. While this abstraction is a powerful basis for data visualization,…