mlsanityData to 7 Oct 2026

Importance of Synthesizing High-quality Data for Text-to-SQL Parsing

arXiv 2212.08785 ↗First posted 17 Dec 2022cs.CLcited by 27

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Abstract (arXiv)

Recently, there has been increasing interest in synthesizing data to improve downstream text-to-SQL tasks. In this paper, we first examined the existing synthesized datasets and discovered that state-of-the-art text-to-SQL algorithms did not further improve on popular benchmarks when trained with augmented synthetic data. We observed two shortcomings: illogical synthetic SQL queries from independent column sampling and arbitrary table joins. To address these issues, we propose a novel synthesis framework that incorporates key relationships from schema, imposes strong typing, and conducts schema-distance-weighted column sampling. We also adopt an intermediate representation (IR) for the SQL-to-text task to further improve the quality of the generated natural language questions. When existing powerful semantic parsers are pre-finetuned on our high-quality synthesized data, our experiments show that these models have significant accuracy boosts on popular benchmarks, including new state-of-the-art performance on Spider.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). Builds on 29 of its 43 references that are in the map. First posted on arXiv 17 Dec 2022.

Builds on

29 of 43 references.

References · 29

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  2. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  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. 2019RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parserscited by 420
  6. 2021PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Modelscited by 402
  7. 2022UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Modelscited by 231
  8. 2019Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representationcited by 210
  9. 2020GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsingcited by 171
  10. 2020Compositional Generalization and Natural Language Variation: Can a Semantic Parsing Approach Handle Both?cited by 147
  11. 2021LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relationscited by 119
  12. 2016Data Recombination for Neural Semantic Parsingcited by 111
17 more
  1. 2017Learning a Neural Semantic Parser from User Feedbackcited by 98
  2. 2020SmBoP: Semi-autoregressive Bottom-up Semantic Parsingcited by 97
  3. 2022RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQLcited by 85
  4. 2019Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questionscited by 80
  5. 2020Grounded Adaptation for Zero-shot Executable Semantic Parsingcited by 76
  6. 2018SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Taskcited by 74
  7. 2021Natural SQL: Making SQL Easier to Infer from Natural Language Specificationscited by 66
  8. 2020Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Trainingcited by 65
  9. 2020On the Potential of Lexico-logical Alignments for Semantic Parsing to SQL Queriescited by 59
  10. 2021GitTables: A Large-Scale Corpus of Relational Tablescited by 44
  11. 2020Optimizing Deeper Transformers on Small Datasetscited by 41
  12. 2021Learning to Synthesize Data for Semantic Parsingcited by 41
  13. 2022S2SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parserscited by 41
  14. 2021Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsingcited by 32
  15. 2018Question Generation from SQL Queries Improves Neural Semantic Parsingcited by 29
  16. 2021Hierarchical Neural Data Synthesis for Semantic Parsingcited by 11
  17. 2017Type- and Content-Driven Synthesis of SQL Queries from Natural Languagecited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 5; 2024: 9; 2025: 10; 2026: 3.

27 papers.

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  1. 2023Teaching Large Language Models to Self-Debugcited by 963
  2. 2023DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correctioncited by 465
  3. 2024CodeS: Towards Building Open-source Language Models for Text-to-SQLcited by 183
  4. 2025OmniSQL: Synthesizing High-quality Text-to-SQL Data at Scalecited by 90
  5. 2024A Survey of Text-to-SQL in the Era of LLMs: Where are we, and where are we going?cited by 54
  6. 2023Selective Demonstrations for Cross-domain Text-to-SQLcited by 21
  7. 2023Interactive Text-to-SQL Generation via Editable Step-by-Step Explanationscited by 19
  8. 2024PRACTIQ: A Practical Conversational Text-to-SQL dataset with Ambiguous and Unanswerable Queriescited by 17
  9. 2024SQL-GEN: Bridging the Dialect Gap for Text-to-SQL Via Synthetic Data And Model Mergingcited by 16
  10. 2024Evaluating the Data Model Robustness of Text-to-SQL Systems Based on Real User Queriescited by 12
  11. 2024You Only Read Once (YORO): Learning to Internalize Database Knowledge for Text-to-SQLcited by 11
  12. 2024EPI-SQL: Enhancing Text-to-SQL Translation with Error-Prevention Instructionscited by 8
15 more
  1. 2025SPFT-SQL: Enhancing Large Language Model for Text-to-SQL Parsing by Self-Play Fine-Tuningcited by 5
  2. 2024Improving Generalization in Semantic Parsing by Increasing Natural Language Variationcited by 4
  3. 2025SAFE-SQL: Self-Augmented In-Context Learning with Fine-grained Example Selection for Text-to-SQLcited by 4
  4. 2025End-to-End Text-to-SQL with Dataset Selection: Leveraging LLMs for Adaptive Query Generationcited by 2
  5. 2025AGRO-SQL: Agentic Group-Relative Optimization with High-Fidelity Data Synthesiscited by 2
  6. 2026CSR-RAG: An Efficient Retrieval System for Text-to-SQL on the Enterprise Scalecited by 2
  7. 2024Relational Database Augmented Large Language Modelcited by 1
  8. 2025REFORMER: A ChatGPT-Driven Data Synthesis Framework Elevating Text-to-SQL Modelscited by 1
  9. 2025SQL-Exchange: Transforming SQL Queries Across Domainscited by 1
  10. 2023Translating Natural Language Queries to SQL Using the T5 Modelcited by 0
  11. 2025StreamLink: Large-Language-Model Driven Distributed Data Engineering Systemcited by 0
  12. 2025Skeletons Matter: Dynamic Data Augmentation for Text-to-Querycited by 0
  13. 2025Bridging Global Intent with Local Details: A Hierarchical Representation Approach for Semantic Validation in Text-to-SQLcited by 0
  14. 2026Boundary-Aware NL2SQL: Integrating Reliability through Hybrid Reward and Data Synthesiscited by 0
  15. 2026Knowledge Distillation for Low-Resource Open-source Text-to-SQL Modelcited by 0