WildTableBench: Benchmarking Multimodal Foundation Models on Table Understanding In the Wild
Junzhe Huang · Xiaoxiao Sun · Yan Yang · Yuxuan Hou · Ruotian Zhang · Sirui Li · Hehe Fan · Serena Yeung-Levy · Xin Yu
This paper presents WildTableBench, a question-answering benchmark dedicated to naturally occurring, real-world table images, and shows that current multimodal foundation models still struggle substantially with practical table image understanding.
摘要
Using multimodal foundation models to analyze table images is a high-value yet challenging application in consumer and enterprise scenarios. Despite its importance, current evaluations rely largely on structured-text tables or clean rendered images, leaving the visual complexity of \emph{in-the-wild} table images underexplored. Such images feature varied layouts and diverse domains that demand sophisticated structural perception and numerical reasoning. To bridge this gap, we introduce WildTableBench, a question-answering benchmark dedicated to naturally occurring table images from real-world settings. WildTableBench comprises 402 high-information-density table images collected from online forums and websites across diverse domains, together with 928 manually annotated and verified questions spanning 17 subtypes across five categories. We evaluate 21 frontier proprietary and open-source multimodal foundation models on this benchmark. Only one model exceeds 50\% accuracy, while all remaining models range from 4.0\% to 49.8\%. We further conduct diagnostic analyses to characterize model failures and reveal persistent weaknesses in structural perception and reasoning. These results and analyses provide useful insights into current model capabilities and establish WildTableBench as a valuable diagnostic benchmark for table image understanding.