构建领域专用图表问答数据集，提升模型深层推理能力。
DomainCQA: Crafting Knowledge-Intensive QA from Domain-Specific Charts
- 按复杂度选图，多层级生成问题，专家审核确保质量。
- 天文领域生成1690组问答，482张图表，暴露21个大模型短板。
- 可拓展至生化、经济等多领域，适合评估专业图表理解能力。
图表问答（CQA）用于评估多模态大模型在图表数据上的视觉理解与推理能力。现有基准大多仅测试表面级解析，如读取标签和图例，忽视深层科学推理。我们提出DomainCQA框架，用于构建强调视觉理解与知识密集型推理的领域特定CQA基准。该框架包含复杂度感知的图表选择、多层级问答生成与专家验证。应用于天文学，生成AstroChart数据集，包含1690组问答对和482张图表，揭示21个MLLM在细粒度感知、数值推理与领域知识融合方面的持续缺陷。在AstroChart上微调可提升基础与高级任务表现。生物化学、经济学、医学和社会科学的初步问答集进一步证明DomainCQA的通用性。结果表明，DomainCQA是构建与扩充领域特定图表推理基准的统一流程。
原文摘要 · Abstract (English)
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. Applied to astronomy, DomainCQA yields AstroChart, a benchmark of 1,690 QA pairs over 482 charts, exposing persistent weaknesses in fine-grained perception, numerical reasoning, and domain knowledge integration across 21 MLLMs. Fine-tuning on AstroChart improves performance across fundamental and advanced tasks. Pilot QA sets in biochemistry, economics, medicine, and social science further demonstrate DomainCQA's generality. Together, our results establish DomainCQA as a unified pipeline for constructing and augmenting domain-specific chart reasoning benchmarks.
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