The advent of multi-modal language models (MLLMs) has spurred research into their application across various table understanding tasks. However, their performance in credit table understanding (CTU) for financial credit review remains largely unexplored due to the following barriers: low data consistency, high annotation costs stemming from domain-specific knowledge and complex calculations, and evaluation paradigm gaps between benchmark and real-world scenarios. To address these challenges, we introduce MMFCTUB (Multi-Modal Financial Credit Table Understanding Benchmark), a practical benchmark, encompassing more than 7,600 high quality CTU samples across 5 table types. MMFCTUB employ a minimally supervised pipeline that adheres to inter-table constraints and maintains data distributions consistency. The benchmark leverages capacity-driven questions and mask-and-recovery strategy to evaluate models' cross-table structure perception, domain knowledge utilization, and numerical calculation capabilities. Utilizing MMFCTUB, we conduct comprehensive evaluations of both proprietary and open-source MLLMs, revealing their strengths and limitations in CTU tasks. MMFCTUB serves as a valuable resource for the research community, facilitating rigorous evaluation of MLLMs in the domain of CTU.

翻译：多模态语言模型（MLLMs）的出现推动了其在各类表格理解任务中的应用研究。然而，由于以下障碍，它们在金融信贷审查中的信贷表格理解（CTU）任务上的性能在很大程度上仍未得到充分探索：数据一致性低、领域特定知识与复杂计算导致的高昂标注成本，以及基准测试与真实场景之间的评估范式差距。为应对这些挑战，我们提出了MMFCTUB（多模态金融信贷表格理解基准），这是一个包含5种表格类型、超过7,600个高质量CTU样本的实用基准。MMFCTUB采用一种最小监督的流程，该流程遵循表格间的约束条件并保持数据分布的一致性。该基准利用能力驱动的问题和掩码-恢复策略来评估模型的跨表格结构感知、领域知识利用以及数值计算能力。利用MMFCTUB，我们对专有及开源MLLMs进行了全面评估，揭示了它们在CTU任务中的优势与局限。MMFCTUB为研究社区提供了一个宝贵的资源，有助于在CTU领域对MLLMs进行严格评估。