13 citations · 14 across the 7 of their papers we have counts for
4 papers · 1 filter
DVBench: Benchmarking MLLMs for Understanding Dynamic Charts and Narratives in Data Videos
Bomiao Wang, Zekai Shao, Jiexiang Lan +3
While MLLMs have made significant strides in chart comprehension and video understanding, current evaluations largely isolate these capabilities, leaving a critical gap in understa…
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…
ChartFI: Benchmarking Faithfulness and Insightfulness of Chart Descriptions from Multimodal Large Language Models
Fen Wang, Zekai Shao, Qiman Kang +5
Chart descriptions are essential for accessibility, cross-modal retrieval, and assisting readers in extracting insights from complex visualizations. As multimodal large language mo…
NotebookRAG: Retrieving Multiple Notebooks to Augment the Generation of EDA Notebooks for Crowd-Wisdom
Yi Shan, Yixuan He, Zekai Shao +2
High-quality exploratory data analysis (EDA) is essential in the data science pipeline, but remains highly dependent on analysts' expertise and effort. While recent LLM-based appro…