DocCLS_NMMH: A Benchmark for Native Multi-Modal Hybrid Document Classification in Enterprise Data Security Governance

In the practice of enterprise data security governance, document AI has emerged as a mission-critical component that seeks to underpin the prevention of document leakage via automatic accurate classification and identification of sensitive content. Arising from this, a need to bring document classification benchmark closer to real-world engineering applications is highlighted. This paper identifies the lack of public datasets for native multi-modal hybrid document classification and, accordingly, proposes the dataset DocCLS_NMMH (Native Multi-Modal Hybrid Document Classification) along with its out-of-distribution (OOD) test subset. An experimental study on the proposed dataset demonstrates that current benchmarks have become irrelevant and need to be updated to evaluate native multi-modal hybrid documents. Meanwhile, accuracy degradation in heterogeneous documents and few-shot scenarios is assessed, as all of these are prevalent in the practice. The experimental results demonstrate that LayoutLM achieves a state-of-the-art (SOTA) performance with 98.66% accuracy on DocCLS_NMMH, with only approximately 7% accuracy degradation on its OOD test subset, while training-free models (Qwen2.5-VL-32B and Gemma3-27B) consistently achieve over 95% accuracy across the full dataset. The SOTA performance of these models on our benchmark provides an effective guidance for model selection in real engineering applications.