3 papers
cs.AI2026
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…
cs.CL2026
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…
cs.CL2025
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…