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DeepTable: Structural Attention Biases and Tree Path Encoding for Hierarchical Table Understanding

arXiv · AI, language, vision and robotics · article · Sep 7, 2026 · UTC

Large language models (LLMs) have demonstrated strong performance in table understanding. However, they typically process table content and headers as linearized token sequences. This representation weakens the two-dimensional and hierarchical structural relationships encoded by multi-level row and column headers. Existing parameter-efficient fine-tuning methods incorporate basic row and column information but do not explicitly capture the rich structural dependencies induced by hierarchical table headers. We propose DeepTable, a structure-aware approach for table understanding with LLMs. Deep

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