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

arXiv: https://arxiv.org/abs/2212.08785 · HTML: https://mlsanity.com/paper/2212.08785/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 43 references that are in the map.
- First posted on arXiv 17 Dec 2022.

## Builds on (most cited first, 29 of 29)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task](https://mlsanity.com/paper/1809.08887/) (2018)
- [Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning](https://mlsanity.com/paper/1709.00103/) (2017)
- [RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers](https://mlsanity.com/paper/1911.04942/) (2019)
- [PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models](https://mlsanity.com/paper/2109.05093/) (2021)
- [UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models](https://mlsanity.com/paper/2201.05966/) (2022)
- [Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation](https://mlsanity.com/paper/1905.08205/) (2019)
- [GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing](https://mlsanity.com/paper/2009.13845/) (2020)
- [Compositional Generalization and Natural Language Variation: Can a Semantic Parsing Approach Handle Both?](https://mlsanity.com/paper/2010.12725/) (2020)
- [LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relations](https://mlsanity.com/paper/2106.01093/) (2021)
- [Data Recombination for Neural Semantic Parsing](https://mlsanity.com/paper/1606.03622/) (2016)
- [Learning a Neural Semantic Parser from User Feedback](https://mlsanity.com/paper/1704.08760/) (2017)
- [SmBoP: Semi-autoregressive Bottom-up Semantic Parsing](https://mlsanity.com/paper/2010.12412/) (2020)
- [RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL](https://mlsanity.com/paper/2205.06983/) (2022)
- [Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions](https://mlsanity.com/paper/1909.00786/) (2019)
- [Grounded Adaptation for Zero-shot Executable Semantic Parsing](https://mlsanity.com/paper/2009.07396/) (2020)
- [SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Task](https://mlsanity.com/paper/1810.05237/) (2018)
- [Natural SQL: Making SQL Easier to Infer from Natural Language Specifications](https://mlsanity.com/paper/2109.05153/) (2021)
- [Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training](https://mlsanity.com/paper/2012.10309/) (2020)
- [On the Potential of Lexico-logical Alignments for Semantic Parsing to SQL Queries](https://mlsanity.com/paper/2010.11246/) (2020)
- [GitTables: A Large-Scale Corpus of Relational Tables](https://mlsanity.com/paper/2106.07258/) (2021)
- [Optimizing Deeper Transformers on Small Datasets](https://mlsanity.com/paper/2012.15355/) (2020)
- [Learning to Synthesize Data for Semantic Parsing](https://mlsanity.com/paper/2104.05827/) (2021)
- [S2SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers](https://mlsanity.com/paper/2203.06958/) (2022)
- [Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsing](https://mlsanity.com/paper/2103.02227/) (2021)
- [Question Generation from SQL Queries Improves Neural Semantic Parsing](https://mlsanity.com/paper/1808.06304/) (2018)
- [Hierarchical Neural Data Synthesis for Semantic Parsing](https://mlsanity.com/paper/2112.02212/) (2021)
- [Type- and Content-Driven Synthesis of SQL Queries from Natural Language](https://mlsanity.com/paper/1702.01168/) (2017)

## Built on by (most cited first, 28 of 28)

- [Teaching Large Language Models to Self-Debug](https://mlsanity.com/paper/2304.05128/) (2023)
- [DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction](https://mlsanity.com/paper/2304.11015/) (2023)
- [CodeS: Towards Building Open-source Language Models for Text-to-SQL](https://mlsanity.com/paper/2402.16347/) (2024)
- [OmniSQL: Synthesizing High-quality Text-to-SQL Data at Scale](https://mlsanity.com/paper/2503.02240/) (2025)
- [A Survey of Text-to-SQL in the Era of LLMs: Where are we, and where are we going?](https://mlsanity.com/paper/2408.05109/) (2024)
- [Selective Demonstrations for Cross-domain Text-to-SQL](https://mlsanity.com/paper/2310.06302/) (2023)
- [Interactive Text-to-SQL Generation via Editable Step-by-Step Explanations](https://mlsanity.com/paper/2305.07372/) (2023)
- [PRACTIQ: A Practical Conversational Text-to-SQL dataset with Ambiguous and Unanswerable Queries](https://mlsanity.com/paper/2410.11076/) (2024)
- [SQL-GEN: Bridging the Dialect Gap for Text-to-SQL Via Synthetic Data And Model Merging](https://mlsanity.com/paper/2408.12733/) (2024)
- [Evaluating the Data Model Robustness of Text-to-SQL Systems Based on Real User Queries](https://mlsanity.com/paper/2402.08349/) (2024)
- [You Only Read Once (YORO): Learning to Internalize Database Knowledge for Text-to-SQL](https://mlsanity.com/paper/2409.12172/) (2024)
- [EPI-SQL: Enhancing Text-to-SQL Translation with Error-Prevention Instructions](https://mlsanity.com/paper/2404.14453/) (2024)
- [SPFT-SQL: Enhancing Large Language Model for Text-to-SQL Parsing by Self-Play Fine-Tuning](https://mlsanity.com/paper/2509.03937/) (2025)
- [Improving Generalization in Semantic Parsing by Increasing Natural Language Variation](https://mlsanity.com/paper/2402.08666/) (2024)
- [SAFE-SQL: Self-Augmented In-Context Learning with Fine-grained Example Selection for Text-to-SQL](https://mlsanity.com/paper/2502.11438/) (2025)
- [End-to-End Text-to-SQL with Dataset Selection: Leveraging LLMs for Adaptive Query Generation](https://mlsanity.com/paper/2508.06387/) (2025)
- [AGRO-SQL: Agentic Group-Relative Optimization with High-Fidelity Data Synthesis](https://mlsanity.com/paper/2512.23366/) (2025)
- [CSR-RAG: An Efficient Retrieval System for Text-to-SQL on the Enterprise Scale](https://mlsanity.com/paper/2601.06564/) (2026)
- [Relational Database Augmented Large Language Model](https://mlsanity.com/paper/2407.15071/) (2024)
- [REFORMER: A ChatGPT-Driven Data Synthesis Framework Elevating Text-to-SQL Models](https://mlsanity.com/paper/2504.04363/) (2025)
- [SQL-Exchange: Transforming SQL Queries Across Domains](https://mlsanity.com/paper/2508.07087/) (2025)
- [Translating Natural Language Queries to SQL Using the T5 Model](https://mlsanity.com/paper/2312.12414/) (2023)
- [StreamLink: Large-Language-Model Driven Distributed Data Engineering System](https://mlsanity.com/paper/2505.21575/) (2025)
- [Skeletons Matter: Dynamic Data Augmentation for Text-to-Query](https://mlsanity.com/paper/2511.18934/) (2025)
- [Bridging Global Intent with Local Details: A Hierarchical Representation Approach for Semantic Validation in Text-to-SQL](https://mlsanity.com/paper/2512.22744/) (2025)
- [Boundary-Aware NL2SQL: Integrating Reliability through Hybrid Reward and Data Synthesis](https://mlsanity.com/paper/2601.10318/) (2026)
- [Knowledge Distillation for Low-Resource Open-source Text-to-SQL Model](https://mlsanity.com/paper/2605.22843/) (2026)
- [FALCON: A Model and Dataset Agnostic Framework for Synthetic Data Generation for NL2SQL Pairs](https://mlsanity.com/paper/2610.03625/) (2026)

## 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.