# Tailoring Table Retrieval from a Field-aware Hybrid Matching Perspective

arXiv: https://arxiv.org/abs/2503.02251 · HTML: https://mlsanity.com/paper/2503.02251/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2025.

## Built on by (most cited first, 7 of 7)

- [CRAFT: Training-Free Cascaded Retrieval for Tabular QA](https://mlsanity.com/paper/2505.14984/) (2025)
- [GLEAN: Grounded Lightweight Evaluation Anchors for Contamination-Aware Tabular Reasoning](https://mlsanity.com/paper/2603.02212/) (2026)
- [FollowTable: A Benchmark for Instruction-Following Table Retrieval](https://mlsanity.com/paper/2605.00400/) (2026)
- [Retrieve Only Relevant Tables Whether Few or Many: Adaptive Table Retrieval Method](https://mlsanity.com/paper/2605.18766/) (2026)
- [HyperPatch: Sequential Knowledge Editing Under n-ary Structural Drift](https://mlsanity.com/paper/2606.03179/) (2026)
- [Semantically Similar, Logically Distinct: Diagnosing the Semantic-Answerability Gap in Table RAG](https://mlsanity.com/paper/2607.17742/) (2026)
- [TableSeek: Structure-Preserving Agentic Evidence Seeking over Heterogeneous Table Corpora](https://mlsanity.com/paper/2609.34157/) (2026)

## Abstract (arXiv)

Table retrieval, essential for accessing information through tabular data, is less explored compared to text retrieval. The row/column structure and distinct fields of tables (including titles, headers, and cells) present unique challenges. For example, different table fields have varying matching preferences: cells may favor finer-grained (word/phrase level) matching over broader (sentence/passage level) matching due to their fragmented and detailed nature, unlike titles. This necessitates a table-specific retriever to accommodate the various matching needs of each table field. Therefore, we introduce a Table-tailored HYbrid Matching rEtriever (THYME), which approaches table retrieval from a field-aware hybrid matching perspective. Empirical results on two table retrieval benchmarks, NQ-TABLES and OTT-QA, show that THYME significantly outperforms state-of-the-art baselines. Comprehensive analyses confirm the differing matching preferences across table fields and validate the design of THYME.