MMTABREAL：多模态表格理解的真实世界基准
MMTABREAL: Real-World Benchmark for Multimodal Table Understanding
AI总结：
针对多模态表格理解，构建了包含500个真实表格和4021个问答对的人工筛选基准MMTABREAL，评估发现现有模型在视觉定位、空间对齐和多步推理上存在20-40%的性能差距。
摘要：
多模态表格，即与图表、地图、图标和颜色编码交织的表格布局，在实际应用中无处不在，但对多模态大语言模型（MLLMs）来说仍然困难。尽管在文本和图像理解方面取得了进展，但对以表格为中心的多模态推理的系统评估仍然有限。我们引入了MMTABREAL，一个多模态表格基准，包含人工筛选的500个真实世界表格及其对应的4021个问答对。MMTABREAL涵盖四种问题类型、五种推理类别和八种结构原型。对最先进模型的评估揭示了显著差距，特别是在视觉定位、空间对齐和多步推理方面，相对于现有基准性能下降了20-40%。这些结果强调了需要更紧密融合视觉与表格结构并支持显式数值/逻辑运算的架构。MMTABREAL仅用于评估，提供了一个严谨、可复现的测试平台，反映了真实世界多模态表格的语言、结构和推理复杂性。
英文摘要：
Multimodal tables i.e. tabular layouts interleaved with charts, maps, icons, and color encodings are ubiquitous in real applications yet remain difficult for Multimodal Large Language Models (MLLMs). Despite advances in text and image understanding, systematic evaluation of table-centric multimodal reasoning is limited. We introduce MMTABREAL, a MultiModal Table Benchmark, human-curated suite of 500 real-world tables paired with 4,021 question-answer pairs. MMTABREAL spans four question types, five reasoning categories, and eight structural archetypes. Evaluations of state-of-the-art models reveal substantial gaps, especially in visual grounding, spatial alignment, and multi-step inference, with 20-40% performance drops relative to existing benchmarks. These results highlight the need for architectures that more tightly fuse vision with tabular structure and support explicit numeric/logical operations. MMTABREAL is released for evaluation only, providing a rigorous, reproducible testbed that reflects the linguistic, structural, and reasoning complexity of real-world multimodal tables.
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