ChartAnno: Benchmarking Multimodal Large Language Models for Chart Annotation Generation
Quick summary
arXiv:2608.03464v2 Announce Type: replace Abstract: Annotations are essential to communicative visualization, helping explain data, emphasize key findings, and guide attention. While multimodal large language models (MLLMs) offer new opportunities for automatic chart annotation authoring, their capabilities in this task remain underexplored. To address this gap, we introduce ChartAnno, a comprehensive benchmark for evaluating MLLMs on chart annotation generation. ChartAnno contains 1,200 real-world charts with paired annotated and unannotated executable code, along with 3,600 annotation instru
Key takeaways
- arXiv:2608.03464v2 Announce Type: replace Abstract: Annotations are essential to communicative visualization, helping explain data, emphasize key findings, and guide attention.
- While multimodal large language models (MLLMs) offer new opportunities for automatic chart annotation authoring, their capabilities in this task remain underexplored.
- To address this gap, we introduce ChartAnno, a comprehensive benchmark for evaluating MLLMs on chart annotation generation.
Why it matters
“ChartAnno: Benchmarking Multimodal Large Language Models for Chart Annotation Generation” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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