mlsanityData to 7 Oct 2026

Is Table Retrieval a Solved Problem? Exploring Join-Aware Multi-Table Retrieval

arXiv 2404.09889 ↗First posted 15 Apr 2024cs.IR cs.AI cs.CLcited by 27

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Abstract (arXiv)

Retrieving relevant tables containing the necessary information to accurately answer a given question over tables is critical to open-domain question-answering (QA) systems. Previous methods assume the answer to such a question can be found either in a single table or multiple tables identified through question decomposition or rewriting. However, neither of these approaches is sufficient, as many questions require retrieving multiple tables and joining them through a join plan that cannot be discerned from the user query itself. If the join plan is not considered in the retrieval stage, the subsequent steps of reasoning and answering based on those retrieved tables are likely to be incorrect. To address this problem, we introduce a method that uncovers useful join relations for any query and database during table retrieval. We use a novel re-ranking method formulated as a mixed-integer program that considers not only table-query relevance but also table-table relevance that requires inferring join relationships. Our method outperforms the state-of-the-art approaches for table retrieval by up to 9.3% in F1 score and for end-to-end QA by up to 5.4% in accuracy.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). Builds on 21 of its 31 references that are in the map. First posted on arXiv 15 Apr 2024.

Builds on

21 of 31 references.

References · 21

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  3. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  4. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  5. 2021Unsupervised Dense Information Retrieval with Contrastive Learningcited by 1,203
  6. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,120
  7. 2017Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learningcited by 679
  8. 2023Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLscited by 667
  9. 2023Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluationcited by 378
  10. 2020Open Question Answering over Tables and Textcited by 163
  11. 2020Detecting Hallucinated Content in Conditional Neural Sequence Generationcited by 151
  12. 2020A Survey of Knowledge-Enhanced Text Generationcited by 119
9 more
  1. 2023A comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capabilitycited by 118
  2. 2021KaggleDBQA: Realistic Evaluation of Text-to-SQL Parserscited by 105
  3. 2021Open Domain Question Answering over Tables via Dense Retrievalcited by 74
  4. 2023Enhancing Few-shot Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategiescited by 44
  5. 2020Table Search Using a Deep Contextualized Language Modelcited by 22
  6. 2022Mixed-modality Representation Learning and Pre-training for Joint Table-and-Text Retrieval in OpenQAcited by 17
  7. 2020Joint Verification and Reranking for Open Fact Checking Over Tablescited by 16
  8. 2022WarpGate: A Semantic Join Discovery System for Cloud Data Warehousescited by 13
  9. 2022Table Retrieval May Not Necessitate Table-specific Model Designcited by 11

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 14; 2026: 10.

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  1. 2024BEAVER: An Enterprise Benchmark for Text-to-SQLcited by 37
  2. 2024Text2SQL is Not Enough: Unifying AI and Databases with TAGcited by 34
  3. 2025EnrichIndex: Using LLMs to Enrich Retrieval Indices Offlinecited by 15
  4. 2025Can we Retrieve Everything All at Once? ARM: An Alignment-Oriented LLM-based Retrieval Methodcited by 10
  5. 2025Tabular Data Understanding with LLMs: A Survey of Recent Advances and Challengescited by 10
  6. 2025Plugging Schema Graph into Multi-Table QA: A Human-Guided Framework for Reducing LLM Reliancecited by 7
  7. 2025Can LLMs Narrate Tabular Data? An Evaluation Framework for Natural Language Representations of Text-to-SQL System Outputscited by 7
  8. 2024Unified Multimodal Interleaved Document Representation for Retrievalcited by 6
  9. 2025Document Intelligence in the Era of Large Language Models: A Surveycited by 5
  10. 2025RASL: Retrieval Augmented Schema Linking for Massive Database Text-to-SQLcited by 4
  11. 2025AutoPK: Leveraging LLMs and a Hybrid Similarity Metric for Advanced Retrieval of Pharmacokinetic Data from Complex Tables and Documentscited by 4
  12. 2025Doc2Chart: Intent-Driven Zero-Shot Chart Generation from Documentscited by 3
15 more
  1. 2025Evaluating NL2SQL via SQL2NLcited by 2
  2. 2025Exploring Multi-Table Retrieval Through Iterative Searchcited by 2
  3. 2026CSR-RAG: An Efficient Retrieval System for Text-to-SQL on the Enterprise Scalecited by 2
  4. 2026CORE-T: COherent REtrieval of Tables for Text-to-SQLcited by 1
  5. 2026Fine-Grained Table Retrieval Through the Lens of Complex Queriescited by 1
  6. 2025ER-RAG: Enhance RAG with ER-Based Unified Modeling of Heterogeneous Data Sourcescited by 0
  7. 2025UNJOIN: Enhancing Multi-Table Text-to-SQL Generation via Schema Simplificationcited by 0
  8. 2025DynaQuery: A Self-Adapting Framework for Querying Structured and Multimodal Datacited by 0
  9. 2026FGTR: Fine-Grained Multi-Table Retrieval via Hierarchical LLM Reasoningcited by 0
  10. 2026FollowTable: A Benchmark for Instruction-Following Table Retrievalcited by 0
  11. 2026Retrieve Only Relevant Tables Whether Few or Many: Adaptive Table Retrieval Methodcited by 0
  12. 2026Latent Bridges for Multi-Table Question Answeringcited by 0
  13. 2026Semantically Similar, Logically Distinct: Diagnosing the Semantic-Answerability Gap in Table RAGcited by 0
  14. 2026TableSeek: Structure-Preserving Agentic Evidence Seeking over Heterogeneous Table Corporacited by 0
  15. 2026JoinGR: Learning to Traverse Join Graphs for Table Retrievalcited by 0