publications
publications by categories in reversed chronological order. generated by jekyll-scholar.
2025
- ACL ARRREaR: Retrieve, Expand and Refine for Effective Multitable RetrievalHimanshu Singhal, Rishita Agarwal, Peter Baile Chen, and 3 more authorsACL ARR 2025 October Submission, 2025
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).
@article{agarwal2025rear, title = {{REaR: Retrieve, Expand and Refine for Effective Multitable Retrieval}}, author = {Singhal, Himanshu and Agarwal, Rishita and Chen, Peter Baile and Choudhury, Manan Roy and Roth, Dan and Gupta, Vivek}, journal = {ACL ARR 2025 October Submission}, year = {2025}, }