SOURCE-LINKED INTELLIGENCE
DEEPCHART: How Far are LLMs from Faithful Data-Science Chart Generation?
Faithful chart generation in real-world data-science workflows requires grounding visualizations in scattered evidence, computing chart-ready quantities, and rendering them accurately. Modern LLMs can produce visually plausible, instruction-compliant charts, yet data-level hallucinations remain difficult to detect in long, noisy, and multimodal contexts. To measure this gap, we introduce DEEPCHART, an expert-annotated benchmark of 1,482 task-conditioned chart-generation instances drawn from real-world scientific papers, financial filings, and ecosystem reports. DEEPCHART formulates chart gener
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-27T07:53:08.000Z
First collected: 2026-09-21T08:51:59.673Z. This is not the publication date.