# Orthogonal Hierarchical Decomposition for Structure-Aware Table Understanding with Large Language Models Source: [Orthogonal Hierarchical Decomposition for Structure-Aware Table Understanding with Large Language Models]() Collection index: [llms.txt]() Data updated: 2026-06-27T05:00:04.727Z Authors: Bin Cao, Huixian Lu, Chenwen Ma, Ting Wang, Ruizhe Li, Jing Fan Published: 2026-02-02T11:22:43.000Z ## Abstract Complex tables with multi-level headers, merged cells and heterogeneous layouts pose persistent challenges for LLMs in both understanding and reasoning. Existing approaches typically rely on table linearization or normalized grid modeling. However, these representations struggle to explicitly capture hierarchical structures and cross-dimensional dependencies, which can lead to misalignment between structural semantics and textual representations for non-standard tables. To address this issue, we propose an Orthogonal Hierarchical Decomposition (OHD) framework that constructs structure-preserving input representations of complex tables for LLMs. OHD introduces an Orthogonal Tree Induction (OTI) method based on spatial--semantic co-constraints, which decomposes irregular tables into a column tree and a row tree to capture vertical and horizontal hierarchical dependencies, respectively. Building on this representation, we design a dual-pathway association protocol to symmetrically reconstruct semantic lineage of each cell, and incorporate an LLM as a semantic arbitrator to align multi-level semantic information. We evaluate OHD framework on two complex table question answering benchmarks, AITQA and HiTab. Experimental results show that OHD consistently outperforms existing representation paradigms across multiple evaluation metrics. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Table Question Answering on AITQA (test)](): Table Question Answering; AITQA (test); EM: 73.83. - [Table Question Answering on HiTab Subset (test)](): Table Question Answering; HiTab Subset (test); EM: 68.37. ## Original sources and implementation [Original paper]() [PDF]()