arXiv：cs.AI· Jia Ling, Yangfan Wang, Chen Tang, Haoming Tan, Yang Yang, Yi Guan, Jingchi Jiang·· 13 小时前
H2Table：用分层超图增强大语言模型进行复杂表格推理
H2Table: Hierarchical Hypergraph-Enhanced Large Language Models for Complex Table Reasoning
AI 导读
H2Table 将复杂表格表示为分层嵌套超图，并设计专用超图编码器在表头（超边）与单元格（节点）之间传递消息，再通过一组可学习查询向量把结构化嵌入传入 LLM。该框架被 EMNLP 2026 主会接收，在 HiTab 数据集嵌套深度为四的高复杂表格上，相较 SOTA 基线平均提升 22.88%。代码已公开。
正文
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: this https URL.
| Comments: | Accepted to EMNLP 2026 (Main Conference). Camera-ready version |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2609.01216 [cs.AI] |
| (or arXiv:2609.01216v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2609.01216 arXiv-issued DOI via DataCite |
Submission history
From: Jia Ling [view email]
[v1]
Tue, 1 Sep 2026 13:19:42 UTC (1,948 KB)
[v2]
Fri, 9 Oct 2026 14:10:37 UTC (1,948 KB)
来源：arXiv：cs.AI · arxiv.org