mlsanityData to 14 Oct 2026

HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data

arXiv 2004.07347 ↗First posted 15 Apr 2020cs.CL cs.AIcited by 264

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Cited by 51 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Existing question answering datasets focus on dealing with homogeneous information, based either only on text or KB/Table information alone. However, as human knowledge is distributed over heterogeneous forms, using homogeneous information alone might lead to severe coverage problems. To fill in the gap, we present HybridQA https://github.com/wenhuchen/HybridQA, a new large-scale question-answering dataset that requires reasoning on heterogeneous information. Each question is aligned with a Wikipedia table and multiple free-form corpora linked with the entities in the table. The questions are designed to aggregate both tabular information and text information, i.e., lack of either form would render the question unanswerable. We test with three different models: 1) a table-only model. 2) text-only model. 3) a hybrid model that combines heterogeneous information to find the answer. The experimental results show that the EM scores obtained by two baselines are below 20%, while the hybrid model can achieve an EM over 40%. This gap suggests the necessity to aggregate heterogeneous information in HybridQA. However, the hybrid model's score is still far behind human performance. Hence, HybridQA can serve as a challenging benchmark to study question answering with heterogeneous information.

Key facts. Cited by 264 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Apr 2020.

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Citing papers per year: 2021: 31; 2022: 43; 2023: 38; 2024: 60; 2025: 54; 2026: 38.

264 papers.

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  1. 2023AgentBench: Evaluating LLMs as Agentscited by 1,267
  2. 2021MuSiQue: Multihop Questions via Single-hop Question Compositioncited by 916
  3. 2021FinQA: A Dataset of Numerical Reasoning over Financial Datacited by 562
  4. 2021TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Financecited by 408
  5. 2022Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoningcited by 350
  6. 2022UL2: Unifying Language Learning Paradigmscited by 295
  7. 2022FOLIO: Natural Language Reasoning with First-Order Logiccited by 235
  8. 2022Making Large Language Models Better Reasoners with Step-Aware Verifiercited by 233
  9. 2022UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Modelscited by 231
  10. 2021MultiModalQA: Complex Question Answering over Text, Tables and Imagescited by 179
  11. 2021FeTaQA: Free-form Table Question Answeringcited by 175
  12. 2021FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured informationcited by 167
38 more
  1. 2024Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generationcited by 162
  2. 2024Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Surveycited by 159
  3. 2023TableLlama: Towards Open Large Generalist Models for Tablescited by 137
  4. 2022MultiHiertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Datacited by 121
  5. 2021HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generationcited by 118
  6. 2022Large Language Models are few(1)-shot Table Reasonerscited by 116
  7. 2023Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Studycited by 116
  8. 2021QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehensioncited by 91
  9. 2025ReFocus: Visual Editing as a Chain of Thought for Structured Image Understandingcited by 83
  10. 2024TableGPT2: A Large Multimodal Model with Tabular Data Integrationcited by 72
  11. 2024CompAct: Compressing Retrieved Documents Actively for Question Answeringcited by 64
  12. 2023Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasonerscited by 63
  13. 2024Are LLMs Capable of Data-based Statistical and Causal Reasoning? Benchmarking Advanced Quantitative Reasoning with Datacited by 63
  14. 2024STaRK: Benchmarking LLM Retrieval on Textual and Relational Knowledge Basescited by 55
  15. 2021MATE: Multi-view Attention for Table Transformer Efficiencycited by 54
  16. 2024Large Language Model for Table Processing: A Surveycited by 52
  17. 2025DeepAnalyze: Agentic Large Language Models for Autonomous Data Sciencecited by 50
  18. 2022Don't Blame the Annotator: Bias Already Starts in the Annotation Instructionscited by 47
  19. 2022Reasoning Like Program Executorscited by 45
  20. 2022PACIFIC: Towards Proactive Conversational Question Answering over Tabular and Textual Data in Financecited by 45
  21. 2021AIT-QA: Question Answering Dataset over Complex Tables in the Airline Industrycited by 44
  22. 2022Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Taskscited by 42
  23. 2024PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models as Decision Makerscited by 42
  24. 2022A Survey on Table Question Answering: Recent Advancescited by 41
  25. 2024UDA: A Benchmark Suite for Retrieval Augmented Generation in Real-world Document Analysiscited by 40
  26. 2021Zero-shot Visual Question Answering using Knowledge Graphcited by 37
  27. 2024PerLTQA: A Personal Long-Term Memory Dataset for Memory Classification, Retrieval, and Synthesis in Question Answeringcited by 34
  28. 2024Text-Tuple-Table: Towards Information Integration in Text-to-Table Generation via Global Tuple Extractioncited by 33
  29. 2022Robust (Controlled) Table-to-Text Generation with Structure-Aware Equivariance Learningcited by 32
  30. 2023TAP4LLM: Table Provider on Sampling, Augmenting, and Packing Semi-structured Data for Large Language Model Reasoningcited by 32
  31. 2025A Survey of Data Agents: Emerging Paradigm or Overstated Hype?cited by 30
  32. 2021ConditionalQA: A Complex Reading Comprehension Dataset with Conditional Answerscited by 29
  33. 2022ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examplescited by 29
  34. 2024StructLM: Towards Building Generalist Models for Structured Knowledge Groundingcited by 29
  35. 2024A Survey of Useful LLM Evaluationcited by 29
  36. 2022HybriDialogue: An Information-Seeking Dialogue Dataset Grounded on Tabular and Textual Datacited by 28
  37. 2023Large Language Models are Complex Table Parserscited by 28
  38. 2025Toward Generalizable Evaluation in the LLM Era: A Survey Beyond Benchmarkscited by 28

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