7 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…
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…
Do Language Model Agents Align with Humans in Rating Visualizations? An Empirical Study
Zekai Shao, Yi Shan, Yixuan He +6
Large language models encode knowledge in various domains and demonstrate the ability to understand visualizations. They may also capture visualization design knowledge and potenti…
Unlocking Scientific Concepts: How Effective Are LLM-Generated Analogies for Student Understanding and Classroom Practice?
Zekai Shao, Siyu Yuan, Lin Gao +3
Teaching scientific concepts is essential but challenging, and analogies help students connect new concepts to familiar ideas. Advancements in large language models (LLMs) enable g…
ChartInsighter: An Approach for Mitigating Hallucination in Time-series Chart Summary Generation with A Benchmark Dataset
Fen Wang, Bomiao Wang, Xueli Shu +4
Effective chart summary can significantly reduce the time and effort decision makers spend interpreting charts, enabling precise and efficient communication of data insights. Previ…