mlsanityData to 14 Oct 2026

TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generation

arXiv 1804.09769 ↗First posted 25 Apr 2018cs.CLcited by 81

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Abstract (arXiv)

Interacting with relational databases through natural language helps users of any background easily query and analyze a vast amount of data. This requires a system that understands users' questions and converts them to SQL queries automatically. In this paper we present a novel approach, TypeSQL, which views this problem as a slot filling task. Additionally, TypeSQL utilizes type information to better understand rare entities and numbers in natural language questions. We test this idea on the WikiSQL dataset and outperform the prior state-of-the-art by 5.5% in much less time. We also show that accessing the content of databases can significantly improve the performance when users' queries are not well-formed. TypeSQL gets 82.6% accuracy, a 17.5% absolute improvement compared to the previous content-sensitive model.

Key facts. Cited by 81 papers in the map (data through 14 Oct 2026). First posted on arXiv 25 Apr 2018.

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Citing papers per year: 2021: 15; 2022: 12; 2023: 11; 2024: 16; 2025: 19; 2026: 8.

81 papers.

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  1. 2023Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLscited by 688
  2. 2023RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQLcited by 195
  3. 2024Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Surveycited by 159
  4. 2021LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relationscited by 119
  5. 2023Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsingcited by 93
  6. 2022RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQLcited by 85
  7. 2024A Survey of Text-to-SQL in the Era of LLMs: Where are we, and where are we going?cited by 56
  8. 2022A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directionscited by 52
  9. 2024Before Generation, Align it! A Novel and Effective Strategy for Mitigating Hallucinations in Text-to-SQL Generationcited by 50
  10. 2022Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expectcited by 47
  11. 2023Skill-Based Few-Shot Selection for In-Context Learningcited by 46
  12. 2021SADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQLcited by 43
38 more
  1. 2022A Survey on Table Question Answering: Recent Advancescited by 41
  2. 2025A Survey on Knowledge-Oriented Retrieval-Augmented Generationcited by 36
  3. 2024E-SQL: Direct Schema Linking via Question Enrichment in Text-to-SQLcited by 35
  4. 2024Semantic Operators: A Declarative Model for Rich, AI-based Data Processingcited by 34
  5. 2024Text2SQL is Not Enough: Unifying AI and Databases with TAGcited by 34
  6. 2022Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsingcited by 30
  7. 2021Awakening Latent Grounding from Pretrained Language Models for Semantic Parsingcited by 28
  8. 2023Large Language Models are Complex Table Parserscited by 28
  9. 2021SeaD: End-to-end Text-to-SQL Generation with Schema-aware Denoisingcited by 27
  10. 2023Natural Language Interfaces for Tabular Data Querying and Visualization: A Surveycited by 27
  11. 2021Improving Text-to-SQL with Schema Dependency Learningcited by 25
  12. 2021Dual Reader-Parser on Hybrid Textual and Tabular Evidence for Open Domain Question Answeringcited by 25
  13. 2025Reliable Text-to-SQL with Adaptive Abstentioncited by 20
  14. 2022N-Best Hypotheses Reranking for Text-To-SQL Systemscited by 19
  15. 2025STaR-SQL: Self-Taught Reasoner for Text-to-SQLcited by 17
  16. 2021mRAT-SQL+GAP:A Portuguese Text-to-SQL Transformercited by 15
  17. 2025Understanding, Detecting, and Repairing Real-World In-Context-Learning-Based Text-to-SQL Errorscited by 14
  18. 2024Evaluating the Data Model Robustness of Text-to-SQL Systems Based on Real User Queriescited by 12
  19. 2024SQL-to-Schema Enhances Schema Linking in Text-to-SQLcited by 12
  20. 2022Deep Learning Driven Natural Languages Text to SQL Query Conversion: A Surveycited by 11
  21. 2024Accurate and Regret-aware Numerical Problem Solver for Tabular Question Answeringcited by 10
  22. 2024A Survey of Large Language Model-Based Generative AI for Text-to-SQL: Benchmarks, Applications, Use Cases, and Challengescited by 10
  23. 2023Towards Understanding the Generalization of Medical Text-to-SQL Models and Datasetscited by 9
  24. 2023Marrying Dialogue Systems with Data Visualization: Interactive Data Visualization Generation from Natural Language Conversationscited by 9
  25. 2025BASE-SQL: A powerful open source Text-To-SQL baseline approachcited by 9
  26. 2022SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parserscited by 7
  27. 2022Speech-to-SQL: Towards Speech-driven SQL Query Generation From Natural Language Questioncited by 6
  28. 2024Unmasking Database Vulnerabilities: Zero-Knowledge Schema Inference Attacks in Text-to-SQL Systemscited by 6
  29. 2025Enhancing Text2Cypher with Schema Filteringcited by 6
  30. 2021GP: Context-free Grammar Pre-training for Text-to-SQL Parserscited by 5
  31. 2021An Investigation Between Schema Linking and Text-to-SQL Performancecited by 5
  32. 2021Pay More Attention to History: A Context Modelling Strategy for Conversational Text-to-SQLcited by 5
  33. 2025Multi-Agent GraphRAG: A Text-to-Cypher Framework for Labeled Property Graphscited by 5
  34. 2022TIE: Topological Information Enhanced Structural Reading Comprehension on Web Pagescited by 4
  35. 2026Learning to Retrieve: Dual-Level Long-Term Memory for Text-to-SQL Agentscited by 4
  36. 2021DBTagger: Multi-Task Learning for Keyword Mapping in NLIDBs Using Bi-Directional Recurrent Neural Networkscited by 3
  37. 2021Leveraging Table Content for Zero-shot Text-to-SQL with Meta-Learningcited by 3
  38. 2023SQLformer: Deep Auto-Regressive Query Graph Generation for Text-to-SQL Translationcited by 3

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