Papers › TableRAG: A Retrieval Augmented Generation Framework for Heterogeneous Document Reasoning

TableRAG: A Retrieval Augmented Generation Framework for Heterogeneous Document Reasoning

12 Jun 2025arXiv:2506.10380archive 2025-07-28

Xiaohan Yu, Pu Jian, Chong Chen

Retrieval-Augmented Generation (RAG) has demonstrated considerable effectiveness in open-domain question answering. However, when applied to heterogeneous documents, comprising both textual and tabular components, existing RAG approaches exhibit critical limitations. The prevailing practice of flattening tables and chunking strategies disrupts the intrinsic tabular structure, leads to information loss, and undermines the reasoning capabilities of LLMs in multi-hop, global queries. To address these challenges, we propose TableRAG, an hybrid framework that unifies textual understanding and complex manipulations over tabular data. TableRAG iteratively operates in four steps: context-sensitive query decomposition, text retrieval, SQL programming and execution, and compositional intermediate answer generation. We also develop HeteQA, a novel benchmark designed to evaluate the multi-hop heterogeneous reasoning capabilities. Experimental results demonstrate that TableRAG consistently outperforms existing baselines on both public datasets and our HeteQA, establishing a new state-of-the-art for heterogeneous document question answering. We release TableRAG at https://github.com/yxh-y/TableRAG/tree/main.

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Tasks

Answer Generation Chunking Open-Domain Question Answering Question Answering RAG Retrieval Retrieval-augmented Generation Text Retrieval

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Methods

Attention Attention Dropout BART BERT BPE Dense Connections Dropout Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention RAG Softmax WordPiece

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