Research paper compares multimodal models for document classification

A new research paper analyzes multimodal approaches for classifying visually-rich documents, comparing transformer and LLM-based architectures. The study evaluated four models, including LayoutLMv3, Donut, and Qwen3, on the RVL-CDIP benchmark. Results indicate that specialized multimodal transformers are more effective than LLM-based approaches for documents with complex layouts, with image information being the most critical factor for classification. AI

IMPACT Provides guidance on selecting effective multimodal architectures and feature combinations for document type classification.

RANK_REASON This is a research paper analyzing and comparing existing models on a benchmark. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Donut
• LayoutLMv3
• Qwen3-32B
• Qwen3-VL-32B-Instruct
• RVL-CDIP

• paper
• model release

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