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arXiv · 2605.00318

Structure-Aware Chunking for Tabular Data in Retrieval-Augmented Generation

Pooja Guttal·Varun Magotra·Vasudeva Mahavishnu·Natasha Chanto·Sidharth Sivaprasad·Manas Gaur
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Abstract

Tabular documents such as CSV and Excel files are widely used in enterprise data pipelines, yet existing chunking strategies for retrieval-augmented generation (RAG) are primarily designed for unstructured text and do not account for tabular structure. We propose a structure-aware tabular chunking (STC) framework that operates on row-level units by constructing a hierarchical Row Tree representation, where each row is encoded as a key-value block. STC performs token-constrained splitting aligned with structural boundaries and applies overlap-free greedy merging to produce dense, non-overlapping chunks. This design preserves semantic relationships between fields within a row while improving token utilization and reducing fragmentation. Across evaluations on the MAUD dataset, STC reduces chunk count by up to 40% and 56% compared to standard recursive and key-value based baselines, respectively, while improving token utilization and processing efficiency. In retrieval benchmarks, STC improves MRR from 0.3576 to 0.5945 in a hybrid setting and increases Recall@1 from 0.366 to 0.754 in BM25-only retrieval. These results demonstrate that preserving structure during chunking improves retrieval performance, highlighting the importance of structure-aware chunking for RAG over tabular data.

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Pooja Guttal, Varun Magotra, Vasudeva Mahavishnu, Natasha Chanto, Sidharth Sivaprasad, Manas Gaur. 2026-05-01. Structure-Aware Chunking for Tabular Data in Retrieval-Augmented Generation. https://arxiv.org/abs/2605.00318

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