TableRAG: A Retrieval Augmented Generation Framework for Heterogeneous Document Reasoning (2506.10380v1)

Abstract: 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.

• The paper introduces TableRAG, a novel framework that integrates SQL-driven reasoning into retrieval-augmented generation to tackle complex heterogeneous document questions.
• It preserves table integrity through an offline database and employs online iterative reasoning that combines query decomposition, text retrieval, and SQL execution.
• Empirical results on the HeteQA benchmark demonstrate that TableRAG outperforms baseline models in multi-hop QA, showcasing enhanced accuracy and efficiency.

TableRAG: A Retrieval-Augmented Generation Framework for Heterogeneous Document Reasoning

The paper introduces TableRAG, a novel framework designed to enhance reasoning capabilities in heterogeneous document-based question answering tasks, a challenge that encompasses both textual and structured tabular data. The authors identify and address critical limitations inherent in existing Retrieval-Augmented Generation (RAG) approaches when dealing with such data types. Notably, traditional methods often disrupt the tabular structure, leading to information loss and diminished reasoning efficacy for LLMs in complex, multi-hop queries.

Framework Overview

The TableRAG framework is structured to preserve table structural integrity while unifying document understanding and textual manipulations over tabular data. TableRAG operates in two main phases:

1. Offline Database Construction: This stage involves the extraction of structured components, forming a database of tables and their relational schema. It establishes mappings from text-based chunks to their originating table schemas, ensuring that these local segments retain contextual relevance.
2. Online Iterative Reasoning: During inference, TableRAG dynamically transitions between query decomposition, text retrieval, SQL programming and execution, and intermediate answer synthesis. This iterative process is crucial for addressing complex queries that require both syntactic and semantic decompositions.

SQL Integration and Iterative Process

A standout feature of TableRAG is its integration of SQL programming as an interface for tabular data reasoning. SQL enables precise execution of table-related queries as monolithic reasoning units, enhancing computational efficiency and fidelity. The framework adopts a compositional reasoning mechanism, where answers derived from SQL execution are cross-examined and validated against text retrieval results to ensure accuracy. By embedding SQL-driven operations within the RAG paradigm, TableRAG effectively bridges the gap between unstructured and structured data reasoning.

HeteQA Benchmark

The authors introduce HeteQA, a benchmark specifically designed to evaluate the multi-hop reasoning capabilities of systems dealing with heterogeneous documents. HeteQA comprises tasks that demand sequential operations and integrates wiki-based information with table queries. The dataset is curated through a human-in-the-loop strategy, ensuring high annotation fidelity. HeteQA serves as a challenging resource for assessing the depth of reasoning in QA systems like TableRAG.

Experimental Insights

Empirical evaluations highlight the robust performance of TableRAG across public benchmarks and the newly developed HeteQA. TableRAG consistently demonstrates superiority over baseline models, outperforming them even in scenarios requiring complex multi-source information synthesis. The iterative reasoning mechanism, alongside SQL execution, underpins the framework’s enhanced accuracy and efficiency. Ablation studies further underscore the significance of each component, with SQL executions proving particularly vital for tasks involving nested operations.

Implications and Future Directions

TableRAG presents significant implications for both practical applications and theoretical advancements in AI. In practice, it offers a rigorous solution for processing and reasoning over complex documents, which is invaluable for domains like finance, research, and data analytics. Theoretically, TableRAG contributes to the evolving landscape of AI, emphasizing the importance of integrating symbolic reasoning with large-scale generative models.

As future work, the exploration of cross-lingual generalization and multilingual support for the HeteQA benchmark is anticipated. Additionally, further refinement of SQL-backed reasoning processes could augment the framework’s adaptability to increasingly diverse data contexts.

In conclusion, TableRAG sets a new benchmark for heterogeneous document reasoning, effectively leveraging retrieval-augmented generation with SQL-driven execution to address the intricate needs of multi-modal question answering tasks.