mlsanityData to 14 Oct 2026

FeTaQA: Free-form Table Question Answering

arXiv 2104.00369 ↗First posted 1 Apr 2021cs.CLcited by 175

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Cited by 24 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Existing table question answering datasets contain abundant factual questions that primarily evaluate the query and schema comprehension capability of a system, but they fail to include questions that require complex reasoning and integration of information due to the constraint of the associated short-form answers. To address these issues and to demonstrate the full challenge of table question answering, we introduce FeTaQA, a new dataset with 10K Wikipedia-based {table, question, free-form answer, supporting table cells} pairs. FeTaQA yields a more challenging table question answering setting because it requires generating free-form text answers after retrieval, inference, and integration of multiple discontinuous facts from a structured knowledge source. Unlike datasets of generative QA over text in which answers are prevalent with copies of short text spans from the source, answers in our dataset are human-generated explanations involving entities and their high-level relations. We provide two benchmark methods for the proposed task: a pipeline method based on semantic-parsing-based QA systems and an end-to-end method based on large pretrained text generation models, and show that FeTaQA poses a challenge for both methods.

Key facts. Cited by 175 papers in the map (data through 14 Oct 2026). Builds on 36 of its 53 references that are in the map. First posted on arXiv 1 Apr 2021.

Builds on

36 of 53 references.

References · 36

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  2. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
  3. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,148
  4. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
  5. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,955
  6. 2018A Call for Clarity in Reporting BLEU Scorescited by 1,799
  7. 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,605
  8. 2020BLEURT: Learning Robust Metrics for Text Generationcited by 1,203
  9. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,139
  10. 2017RACE: Large-scale ReAding Comprehension Dataset From Examinationscited by 861
  11. 2017Reading Wikipedia to Answer Open-Domain Questionscited by 777
  12. 2017Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learningcited by 687
24 more
  1. 2018CoQA: A Conversational Question Answering Challengecited by 683
  2. 2017The NarrativeQA Reading Comprehension Challengecited by 597
  3. 2019ELI5: Long Form Question Answeringcited by 575
  4. 2020KILT: a Benchmark for Knowledge Intensive Language Taskscited by 573
  5. 2015Compositional Semantic Parsing on Semi-Structured Tablescited by 515
  6. 2020TAPAS: Weakly Supervised Table Parsing via Pre-trainingcited by 434
  7. 2019TabFact: A Large-scale Dataset for Table-based Fact Verificationcited by 428
  8. 2020TaBERT: Pretraining for Joint Understanding of Textual and Tabular Datacited by 398
  9. 2020ToTTo: A Controlled Table-To-Text Generation Datasetcited by 268
  10. 2020HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Datacited by 264
  11. 2020GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsingcited by 171
  12. 2021Hurdles to Progress in Long-form Question Answeringcited by 167
  13. 2020Open Question Answering over Tables and Textcited by 163
  14. 2020DART: Open-Domain Structured Data Record to Text Generationcited by 145
  15. 2020Question and Answer Test-Train Overlap in Open-Domain Question Answering Datasetscited by 138
  16. 2018Improving Text-to-SQL Evaluation Methodologycited by 135
  17. 2020Text-to-Text Pre-Training for Data-to-Text Taskscited by 104
  18. 2020Logical Natural Language Generation from Open-Domain Tablescited by 90
  19. 2019Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generationcited by 75
  20. 2020Understanding tables with intermediate pre-trainingcited by 68
  21. 2018Complex Sequential Question Answering: Towards Learning to Converse Over Linked Question Answer Pairs with a Knowledge Graphcited by 67
  22. 2018Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsingcited by 57
  23. 2019Learning to Select, Track, and Generate for Data-to-Textcited by 14
  24. 2020KGPT: Knowledge-Grounded Pre-Training for Data-to-Text Generationcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 4; 2022: 15; 2023: 26; 2024: 51; 2025: 47; 2026: 32.

175 papers.

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  1. 2022ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoningcited by 1,479
  2. 2023AgentBench: Evaluating LLMs as Agentscited by 1,267
  3. 2022Towards Reasoning in Large Language Models: A Surveycited by 596
  4. 2022Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoningcited by 350
  5. 2022UL2: Unifying Language Learning Paradigmscited by 295
  6. 2022FOLIO: Natural Language Reasoning with First-Order Logiccited by 235
  7. 2022UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Modelscited by 231
  8. 2023FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Setscited by 201
  9. 2024Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understandingcited by 188
  10. 2023Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoningcited by 178
  11. 2024Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Surveycited by 159
  12. 2023TableLlama: Towards Open Large Generalist Models for Tablescited by 137
38 more
  1. 2024TableBench: A Comprehensive and Complex Benchmark for Table Question Answeringcited by 128
  2. 2023Chain-of-Knowledge: Grounding Large Language Models via Dynamic Knowledge Adapting over Heterogeneous Sourcescited by 122
  3. 2022MultiHiertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Datacited by 121
  4. 2022Large Language Models are few(1)-shot Table Reasonerscited by 116
  5. 2024SpreadsheetBench: Towards Challenging Real World Spreadsheet Manipulationcited by 111
  6. 2024LayoutLLM: Layout Instruction Tuning with Large Language Models for Document Understandingcited by 95
  7. 2023Retrieving Multimodal Information for Augmented Generation: A Surveycited by 85
  8. 2024TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarioscited by 77
  9. 2024SPIQA: A Dataset for Multimodal Question Answering on Scientific Paperscited by 75
  10. 2024TableGPT2: A Large Multimodal Model with Tabular Data Integrationcited by 72
  11. 2023TIGERScore: Towards Building Explainable Metric for All Text Generation Taskscited by 70
  12. 2024Large Language Model for Table Processing: A Surveycited by 52
  13. 2025DeepAnalyze: Agentic Large Language Models for Autonomous Data Sciencecited by 50
  14. 2024A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Taskscited by 47
  15. 2021AIT-QA: Question Answering Dataset over Complex Tables in the Airline Industrycited by 44
  16. 2024Multimodal Table Understandingcited by 44
  17. 2024PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models as Decision Makerscited by 42
  18. 2022A Survey on Table Question Answering: Recent Advancescited by 41
  19. 2024VisDoM: Multi-Document QA with Visually Rich Elements Using Multimodal Retrieval-Augmented Generationcited by 41
  20. 2024UDA: A Benchmark Suite for Retrieval Augmented Generation in Real-world Document Analysiscited by 40
  21. 2021Turning Tables: Generating Examples from Semi-structured Tables for Endowing Language Models with Reasoning Skillscited by 39
  22. 2024OneChart: Purify the Chart Structural Extraction via One Auxiliary Tokencited by 39
  23. 2024TabSQLify: Enhancing Reasoning Capabilities of LLMs Through Table Decompositioncited by 39
  24. 2025Table-R1: Region-based Reinforcement Learning for Table Understandingcited by 37
  25. 2023MultiTabQA: Generating Tabular Answers for Multi-Table Question Answeringcited by 32
  26. 2022ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examplescited by 29
  27. 2024A Survey of Table Reasoning with Large Language Modelscited by 29
  28. 2024StructLM: Towards Building Generalist Models for Structured Knowledge Groundingcited by 29
  29. 2023Large Language Models are Complex Table Parserscited by 28
  30. 2023TempTabQA: Temporal Question Answering for Semi-Structured Tablescited by 27
  31. 2024Exploring the Impact of Table-to-Text Methods on Augmenting LLM-based Question Answering with Domain Hybrid Datacited by 24
  32. 2023Effective Distillation of Table-based Reasoning Ability from LLMscited by 23
  33. 2024CABINET: Content Relevance based Noise Reduction for Table Question Answeringcited by 23
  34. 2024ProTrix: Building Models for Planning and Reasoning over Tables with Sentence Contextcited by 23
  35. 2025Pneuma: Leveraging LLMs for Tabular Data Representation and Retrieval in an End-to-End Systemcited by 23
  36. 2023DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AIcited by 22
  37. 2021Data-QuestEval: A Referenceless Metric for Data-to-Text Semantic Evaluationcited by 21
  38. 2024Tree-of-Table: Unleashing the Power of LLMs for Enhanced Large-Scale Table Understandingcited by 20

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