让大模型更懂复杂表格的层级结构。
Orthogonal Hierarchical Decomposition for Structure-Aware Table Understanding with Large Language Models
- 用垂直和水平双树结构分解表格，保留层级关系。
- 在两个基准上性能超越现有方法，最高提升12.3%。
- 适合需要精准理解表格结构的科研与数据分析场景。
包含多级标题、合并单元格和异构布局的复杂表格给大语言模型（LLMs）的理解与推理带来持续挑战。现有方法通常依赖表格线性化或标准化网格建模，但这些表示难以显式捕捉层级结构和跨维度依赖，导致非标准表格中结构语义与文本表示不一致。为此，我们提出正交层级分解（OHD）框架，为LLMs构建保持结构的复杂表格输入表示。OHD引入基于空间-语义协同约束的正交树诱导（OTI）方法，将不规则表格分解为列树和行树，分别捕获垂直和水平层级依赖。在此基础上，设计双路径关联协议对每个单元格的语义谱系进行对称重建，并引入LLM作为语义仲裁者对齐多级语义信息。我们在两个复杂表格问答基准AItQA和HiTab上评估了OHD框架。实验结果表明，OHD在多个评估指标上持续优于现有表示范式。
原文摘要 · Abstract (English)
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.
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