REaR: Retrieve, Expand and Refine for Effective Multitable Retrieval

• URL: http://arxiv.org/abs/2511.00805v1
• Date: Sun, 02 Nov 2025 05:01:04 GMT
• Title: REaR: Retrieve, Expand and Refine for Effective Multitable Retrieval
• Authors: Rishita Agarwal, Himanshu Singhal, Peter Baile Chen, Manan Roy Choudhury, Dan Roth, Vivek Gupta,
• Abstract summary: REAR (Retrieve, Expand and Refine) is a three-stage framework for efficient, high-fidelity multi-table retrieval.<n>Rear retrieves query-aligned tables, expands these with structurally joinable tables, and refines them by pruning noisy or weakly related candidates.<n>Rear is retriever-agnostic and consistently improves dense/sparse retrievers on complex table QA datasets.
• Score: 46.38349148493421
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Answering natural language queries over relational data often requires retrieving and reasoning over multiple tables, yet most retrievers optimize only for query-table relevance and ignore table table compatibility. We introduce REAR (Retrieve, Expand and Refine), a three-stage, LLM-free framework that separates semantic relevance from structural joinability for efficient, high-fidelity multi-table retrieval. REAR (i) retrieves query-aligned tables, (ii) expands these with structurally joinable tables via fast, precomputed column-embedding comparisons, and (iii) refines them by pruning noisy or weakly related candidates. Empirically, REAR is retriever-agnostic and consistently improves dense/sparse retrievers on complex table QA datasets (BIRD, MMQA, and Spider) by improving both multi-table retrieval quality and downstream SQL execution. Despite being LLM-free, it delivers performance competitive with state-of-the-art LLM-augmented retrieval systems (e.g.,ARM) while achieving much lower latency and cost. Ablations confirm complementary gains from expansion and refinement, underscoring REAR as a practical, scalable building block for table-based downstream tasks (e.g., Text-to-SQL).

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