DomainCQA: Crafting Knowledge-Intensive QA from Domain-Specific Charts

Understand

Chart Question Answering (CQA) evaluates Multimodal Large Language Models (MLLMs) on visual understanding and reasoning over chart data.

• However, existing benchmarks mostly test surface-level parsing, such as reading labels and legends, while overlooking deeper scientific reasoning.
• We propose DomainCQA, a framework for constructing domain-specific CQA benchmarks that emphasize both visual comprehension and knowledge-intensive reasoning.
• It integrates complexity-aware chart selection, multitier QA generation, and expert validation.