Table Retrieval

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Table retrieval studies how to find the tables and schema elements needed to answer a question or generate a query across large databases and table collections. Common approaches combine BM25 or dense embeddings with table descriptions, query decomposition, LLM reranking, and structural signals such as join graphs; some systems instead use agents to inspect and refine candidates. Benchmarks including Spider, BIRD, and table question-answering datasets test retrieval quality and its downstream effect on SQL execution or answer accuracy. A central challenge is that topical similarity does not guarantee the retrieved tables contain answerable evidence, while schema constraints, joins, serialization choices, and context budgets all affect what should be selected.

Latest papers

19 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

JoinGR: Learning to Traverse Join Graphs for Table Retrieval
Oct 1, 2026Sandipan De, Abhijit Chakraborty, Sambaran Bandyopadhyay +1Graph-Based Retrieval Table Retrieval
Arizona State University · Adobe Research, India
TableSeek: Structure-Preserving Agentic Evidence Seeking over Heterogeneous Table Corpora
Sep 28, 2026Jiaming Tian, Liyao Li, Wentao Ye +5Agentic Retrieval Table Retrieval
Zhejiang University · Bank of Hangzhou Co., Ltd. · Zhejiang Lab
BudgetSchemaBench: A Budget-Swept Diagnostic for Schema Context in Text-to-SQL
Sep 8, 2026Chen ShenText-to-SQL Text-to-SQL Evaluation
Megagon Labs
Polaris: Learning to Generate Table Descriptions from Retrieval Feedback
Aug 17, 2026Ting Cai, Tuan Minh Phan, AnHai DoanTable Retrieval
University of Wisconsin-Madison
EKLAV: Elicited Key CoT Learning via Answer only superVision
Jul 28, 2026Adarsh Singh, Aman Singh, Kushal Raj Bhandari +3Table QA CoT Distillation
Arizona State University · Rensselaer Polytechnic Institute · Scale AI
TEmBed-T: A Multi-Dimensional Benchmark for Table-Level Embeddings
Jul 27, 2026Ayeen Poostforoushan, Liane Vogel, Carsten BinnigTabular Representation Learning Information Retrieval
Independent Researcher · Technical University of Darmstadt · Technical University of Darmstadt & DFKI & hessian.AI
Semantically Similar, Yet Not Answerable: Diagnosing the Semantic-Answerability Gap in Table RAG
Jul 20, 2026Jiaming Tian, Liyao Li, Wentao Ye +6Table QA Retrieval-Augmented Generation
Zhejiang University · Bank of Hangzhou Co., Ltd. · Zhejiang Lab
Finding the Right Tables and Columns: A Benchmark and Corpus-Adaptive Embeddings for SQL Schema Retrieval
Jul 14, 2026Qingcheng Zeng, Puxuan Yu, Aman Mehta +2Schema Linking Text-to-SQL
Northwestern University · Snowflake Inc.
Schema-First Retrieval: Embedding Catalogs for Natural Language Analytics
Jun 23, 2026Adarsh Agrawal, Shashank IndukuriTable Retrieval Text-to-SQL
RACT: Retrieval Augmented Column-Table Learning and Prediction for Multi-Table Schema Matching
Jun 5, 2026Leonard Traeger, Enas Khwaileh, Andreas Behrend +1Table Retrieval Semantic Mapping
University of Maryland, Baltimore County, USA · Utrecht University Utrecht, The Netherlands · Technical University of Cologne Cologne, Germany
PIPER: Content-Based Table Search via profiling and LLM-Generated Pseudoqueries
May 18, 2026Riccardo Terrenzi, Matteo Falconi, Serkan Ayvaz +1Information Retrieval Table Retrieval
Centre for Industrial Software, University of Southern Denmark, Sønderborg, Denmark · Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy
TabEmbed: Benchmarking and Learning Generalist Embeddings for Tabular Understanding
May 6, 2026Minjie Qiang, Mingming Zhang, Xiaoyi Bao +5Tabular Foundation Models Tabular ML
Natural Language Processing Lab, Soochow University, Suzhou, China · Ant Group, Hangzhou, China · The Hong Kong Polytechnic University, Hong Kong, China
FollowTable: A Benchmark for Instruction-Following Table Retrieval
May 1, 2026Rihui Jin, Yuchen Lu, Ting Zhang +7Information Retrieval Table Retrieval
Southeast University Nanjing, China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education · Huawei Noah’s Ark Lab Shenzhen, China
Improving Robustness of Tabular Retrieval via Representational Stability
Apr 27, 2026Kushal Raj Bhandari, Adarsh Singh, Jianxi Gao +2Retrieval Robustness Information Retrieval
Department of Computer Science Arizona State University Tempe, Arizona, USA · ScaleAI San Francisco, CA, USA
Towards Platonic Representation for Table Reasoning: A Foundation for Permutation-Invariant Retrieval
Apr 13, 2026Willy Carlos Tchuitcheu, Tan Lu, Ann DoomsTable Reasoning Tabular Representation Learning
Department of Mathematics and Data Science, Vrije Universiteit Brussel (VUB), Brussels, Belgium · Data Science Lab, Royal Library of Belgium (KBR), Brussels, Belgium
Retrieve Only Relevant Tables Whether Few or Many: Adaptive Table Retrieval Method
Apr 12, 2026Taehee Kim, Seungbin Yang, Jihwan Kim +1Table Retrieval Adaptive Retrieval
KAIST AI · Letsur
FGTR: Fine-Grained Multi-Table Retrieval via Hierarchical LLM Reasoning
Mar 13, 2026Chaojie Sun, Bin Cao, Tiantian Li +3Table Retrieval LLM Reasoning
Zhejiang University of Technology Hangzhou, China · University of Aberdeen Aberdeen, UK
Fine-Grained Table Retrieval Through the Lens of Complex Queries
Mar 7, 2026Wojciech Kosiuk, Xingyu Ji, Yeounoh Chung +2Table Retrieval Information Retrieval
University of Amsterdam · UC Berkeley · Google +1
CORE-T: COherent REtrieval of Tables for Text-to-SQL
Jan 19, 2026Hassan Soliman, Vivek Gupta, Dan Roth +1Text-to-SQL Information Retrieval
Ubiquitous Knowledge Processing Lab (UKP Lab), Department of Computer Science TU Darmstadt and National Research Center for Applied Cybersecurity ATHENE, Germany · Arizona State University · University of Pennsylvania

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JoinGR: Learning to Traverse Join Graphs for Table Retrieval

Sandipan De, Abhijit Chakraborty, Sambaran Bandyopadhyay, Vivek Gupta

Arizona State University · Adobe Research, India

Graph-Based Retrieval Table Retrieval Database Systems

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Retrieving the right tables is a prerequisite for Text-to-SQL over realistic databases. Dense table retrievers rank schema elements independently, but this ignores a key source of evidence: some required tables are not mentioned in the question and become identifiable only through their join relationships to already relevant tables. We introduce JOINGR, a join-aware table retrieval method that treats the database join graph as the retrieval space. Columns are represented as graph nodes, while intra-table and foreign-key relationships are represented as typed edges. Given a question, JOINGR selects semantically similar anchor tables, traverses join edges with a query-conditioned scorer, and aggregates the resulting edge deposits into table scores. The scorer is a lightweight MLP on top of frozen query, node, and edge embeddings, trained with a pairwise margin loss over gold tables. On BIRD and Spider datasets, JOINGR is competitive with the strongest retrieval baselines. On BEAVER, a challenging enterprise benchmark with multi-hop table requirements, JOINGR substantially improves recall over dense retrieval and re-ranking baselines. Cross-domain experiments show that the learned scorer transfers across benchmarks, indicating that the method captures reusable joingraph traversal behavior.