H2Table: Hierarchical Hypergraph-Enhanced Large Language Models for Complex Table Reasoning

2026-09-01

AI Summary

本文提出H2Table框架，通过将复杂表格表示为层次嵌套超图并设计专用编码器来解决现代大语言模型在处理表格时忽略其二维和层次结构的问题。

Abstract

Tables are ubiquitous across diverse domains, yet reasoning over them remains a significant challenge for modern large language models (LLMs). Current approaches typically linearize tables into sequences, inherently overlooking their intrinsic two-dimensional and hierarchical structure. To address this, we propose H2Table (Hierarchical Hypergraph-Enhanced Table Reasoning), a novel framework that represents complex tables as hierarchical nested hypergraphs. To process this representation, we design a tailored hypergraph encoder to facilitate message passing between hyperedges (headers) and nodes (cells), thereby perceiving the semantic entailment relationships between them within complex tables. Furthermore, we introduce a set of learnable query vectors acting as a lightweight bridge to extract representative structural embeddings from the encoder into the LLM. Experimental results demonstrate that our approach effectively handles complex table question answering tasks with hierarchical nested headers. Notably, on the HiTab dataset, H2Table achieves an average improvement of 22.88% over state-of-the-art baselines on highly complex tables with a nesting depth of four. Our code is available at: https://github.com/lila120/h2table.

Problem

• large language models
• table reasoning
• hierarchical structure
• complex tables

Innovation

• Hierarchical Hypergraph
• Complex Table Reasoning
• Hypergraph Encoder
• Learnable Query Vectors
• Structural Embeddings