DocLLM: A Layout-Aware Generative Language Model for Multimodal Document Understanding (Wang et al., 2023)

DocLLM is a layout-aware generative language model designed to interpret visually rich documents such as forms, invoices, receipts, and reports by combining OCR-extracted text with bounding-box coordinates rather than relying on image encoders. Built on an autoregressive transformer, it uses disentangled attention to model text-to-text, text-to-spatial, and spatial-to-spatial relationships, enabling it to connect document content with its two-dimensional layout. Its self-supervised pre-training method masks text blocks and requires the model to infill them from surrounding textual and spatial context, while later instruction tuning adapts it for visual question answering, key information extraction, natural language inference, and document classification. Evaluations across 16 datasets found that the 7-billion-parameter DocLLM outperformed comparable language-model baselines in most same-dataset and cross-dataset settings, particularly on layout-intensive tasks, and the researchers propose adding lightweight vision capabilities in future work.

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