Making Multimodal LLMs Reliable Chart Data Extractors: A Benchmark and Training Framework
Chart data extraction, which reverse-engineers data tables from chart images, is essential for reproducibility, analysis, retrieval, and redesign. Existing interactive tools are reliable but tedious, and mixed-initiative systems, while more efficient, lack generalizability. Recent multimodal large language models (MLLMs) offer a unified interface for chart interpretation, yet their ability to extract accurate data tables, especially without visible labels, remains unclear. We build a benchmark featuring diverse real-world charts without data labels to evaluate this capability. Results show that, while current MLLMs reliably reconstruct table structures, they struggle with precise value re...
| Authors | Yuchen He, Peizhi Ying, Liqi Cheng et al. |
|---|---|
| Primary category | cs.HC |
| Published | 29 June 2026 |
| Last updated at source | 29 June 2026 |
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