4 papers
ChartAnno: Evaluating MLLMs for Chart Annotation Generation
Zhenghan Chen, Zekai Shao, Lidan Tan +10
Multimodal large language models (MLLMs) have made significant progress in chart understanding, generation, and editing, but their ability to annotate existing charts remains under…
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…
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…
: 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 \…