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A Survey on Table Question Answering: Recent Advances

arXiv 2207.05270 ↗First posted 12 Jul 2022cs.CL cs.AIcited by 41

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Abstract (arXiv)

Table Question Answering (Table QA) refers to providing precise answers from tables to answer a user's question. In recent years, there have been a lot of works on table QA, but there is a lack of comprehensive surveys on this research topic. Hence, we aim to provide an overview of available datasets and representative methods in table QA. We classify existing methods for table QA into five categories according to their techniques, which include semantic-parsing-based, generative, extractive, matching-based, and retriever-reader-based methods. Moreover, as table QA is still a challenging task for existing methods, we also identify and outline several key challenges and discuss the potential future directions of table QA.

Key facts. Cited by 41 papers in the map (data through 7 Oct 2026). Builds on 44 of its 52 references that are in the map. First posted on arXiv 12 Jul 2022.

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44 of 52 references.

References · 44

  1. 2020Longformer: The Long-Document Transformercited by 3,362
  2. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,203
  3. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,120
  4. 2017Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learningcited by 679
  5. 2021FinQA: A Dataset of Numerical Reasoning over Financial Datacited by 552
  6. 2015Compositional Semantic Parsing on Semi-Structured Tablescited by 509
  7. 2020TAPAS: Weakly Supervised Table Parsing via Pre-trainingcited by 432
  8. 2019RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parserscited by 420
  9. 2021TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Financecited by 402
  10. 2020HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Datacited by 263
  11. 2021FeTaQA: Free-form Table Question Answeringcited by 174
  12. 2020Open Question Answering over Tables and Textcited by 163
32 more
  1. 2016Language to Logical Form with Neural Attentioncited by 158
  2. 2017SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learningcited by 135
  3. 2021LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relationscited by 119
  4. 2021HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generationcited by 117
  5. 2018Coarse-to-Fine Decoding for Neural Semantic Parsingcited by 110
  6. 2022TableFormer: Robust Transformer Modeling for Table-Text Encodingcited by 90
  7. 2019A Comprehensive Exploration on WikiSQL with Table-Aware Word Contextualizationcited by 88
  8. 2018TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generationcited by 81
  9. 2019A Discrete Hard EM Approach for Weakly Supervised Question Answeringcited by 78
  10. 2021Open Domain Question Answering over Tables via Dense Retrievalcited by 74
  11. 2020UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answeringcited by 61
  12. 2018Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsingcited by 57
  13. 2021MATE: Multi-view Attention for Table Transformer Efficiencycited by 54
  14. 2018Robust Text-to-SQL Generation with Execution-Guided Decodingcited by 51
  15. 2020A Survey on Complex Question Answering over Knowledge Base: Recent Advances and Challengescited by 47
  16. 2021AIT-QA: Question Answering Dataset over Complex Tables in the Airline Industrycited by 44
  17. 2015Neural Programmer: Inducing Latent Programs with Gradient Descentcited by 32
  18. 2021Capturing Row and Column Semantics in Transformer Based Question Answering over Tablescited by 31
  19. 2019Learning Semantic Parsers from Denotations with Latent Structured Alignments and Abstract Programscited by 26
  20. 2021Dual Reader-Parser on Hybrid Textual and Tabular Evidence for Open Domain Question Answeringcited by 25
  21. 2019Answering Conversational Questions on Structured Data without Logical Formscited by 24
  22. 2019X-SQL: reinforce schema representation with contextcited by 22
  23. 2020Text-based Question Answering from Information Retrieval and Deep Neural Network Perspectives: A Surveycited by 21
  24. 2021Representations for Question Answering from Documents with Tables and Textcited by 21
  25. 2021TSQA: Tabular Scenario Based Question Answeringcited by 21
  26. 2016TabMCQ: A Dataset of General Knowledge Tables and Multiple-choice Questionscited by 18
  27. 2018Semantic Parsing with Syntax- and Table-Aware SQL Generationcited by 17
  28. 2016Learning a Natural Language Interface with Neural Programmercited by 14
  29. 2018Adversarial TableQA: Attention Supervision for Question Answering on Tablescited by 10
  30. 2022Reasoning over Hybrid Chain for Table-and-Text Open Domain QAcited by 10
  31. 2019BERTSel: Answer Selection with Pre-trained Modelscited by 5
  32. 2018Policy Shaping and Generalized Update Equations for Semantic Parsing from Denotationscited by 4

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Citing papers per year: 2022: 1; 2023: 5; 2024: 19; 2025: 12; 2026: 4.

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  1. 2024Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understandingcited by 185
  2. 2024Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Surveycited by 157
  3. 2024TableBench: A Comprehensive and Complex Benchmark for Table Question Answeringcited by 127
  4. 2024Large Language Models Meet NLP: A Surveycited by 102
  5. 2024Large Language Model for Table Processing: A Surveycited by 52
  6. 2025Representation Learning for Tabular Data: A Comprehensive Surveycited by 36
  7. 2025Table-R1: Region-based Reinforcement Learning for Table Understandingcited by 35
  8. 2023MultiTabQA: Generating Tabular Answers for Multi-Table Question Answeringcited by 32
  9. 2024A Survey of Table Reasoning with Large Language Modelscited by 29
  10. 2023Large Language Models are Complex Table Parserscited by 28
  11. 2023TabLib: A Dataset of 627M Tables with Contextcited by 23
  12. 2024AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmarkcited by 23
29 more
  1. 2022OFASys: A Multi-Modal Multi-Task Learning System for Building Generalist Modelscited by 17
  2. 2024TQA-Bench: Evaluating LLMs for Multi-Table Question Answeringcited by 17
  3. 2024MM-PhyQA: Multimodal Physics Question-Answering With Multi-Image CoT Promptingcited by 15
  4. 2025LLM/Agent-as-Data-Analyst: A Surveycited by 15
  5. 2025Multimodal Tabular Reasoning with Privileged Structured Informationcited by 14
  6. 2024AutoPrep: Natural Language Question-Aware Data Preparation with a Multi-Agent Frameworkcited by 12
  7. 2024Accurate and Regret-aware Numerical Problem Solver for Tabular Question Answeringcited by 10
  8. 2025RoT: Enhancing Table Reasoning with Iterative Row-Wise Traversalscited by 10
  9. 2024Knowledge-Aware Reasoning over Multimodal Semi-structured Tablescited by 9
  10. 2023cTBLS: Augmenting Large Language Models with Conversational Tablescited by 7
  11. 2024Table Question Answering for Low-resourced Indic Languagescited by 7
  12. 2025Plugging Schema Graph into Multi-Table QA: A Human-Guided Framework for Reducing LLM Reliancecited by 7
  13. 2024Navigating the Landscape of Hint Generation Research: From the Past to the Futurecited by 6
  14. 2024DocTabQA: Answering Questions from Long Documents Using Tablescited by 6
  15. 2025Mixture-of-Minds: Multi-Agent Reinforcement Learning for Table Understandingcited by 6
  16. 2023Doc2SoarGraph: Discrete Reasoning over Visually-Rich Table-Text Documents via Semantic-Oriented Hierarchical Graphscited by 5
  17. 2025Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluationcited by 4
  18. 2024How Robust are the Tabular QA Models for Scientific Tables? A Study using Customized Datasetcited by 3
  19. 2024TabVer: Tabular Fact Verification with Natural Logiccited by 3
  20. 2025TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answeringcited by 3
  21. 2024Enhancing Numerical Reasoning with the Guidance of Reliable Reasoning Processescited by 1
  22. 2024Training Table Question Answering via SQL Query Decompositioncited by 1
  23. 2025Team Anotheroption at SemEval-2025 Task 8: Bridging the Gap Between Open-Source and Proprietary LLMs in Table QAcited by 1
  24. 2025Agentic LLMs for Question Answering over Tabular Datacited by 1
  25. 2025DeKeyNLU: Enhancing Natural Language to SQL Generation through Task Decomposition and Keyword Extractioncited by 1
  26. 2026An Empirical Investigation of Robustness in Large Language Models under Tabular Distortionscited by 0
  27. 2026DataFactory: Collaborative Multi-Agent Framework for Advanced Table Question Answeringcited by 0
  28. 2026Efficient Table QA via TableGrid Navigation and Progressive Inference Promptingcited by 0
  29. 2026The Importance of Encoder Choice:A Tabular-Image Studycited by 0