Data to 7 Oct 2026

Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation

arXiv 1905.08205 ↗First posted 20 May 2019cs.CLcited by 210

Not on the map. Why?

Abstract (arXiv)

We present a neural approach called IRNet for complex and cross-domain Text-to-SQL. IRNet aims to address two challenges: 1) the mismatch between intents expressed in natural language (NL) and the implementation details in SQL; 2) the challenge in predicting columns caused by the large number of out-of-domain words. Instead of end-to-end synthesizing a SQL query, IRNet decomposes the synthesis process into three phases. In the first phase, IRNet performs a schema linking over a question and a database schema. Then, IRNet adopts a grammar-based neural model to synthesize a SemQL query which is an intermediate representation that we design to bridge NL and SQL. Finally, IRNet deterministically infers a SQL query from the synthesized SemQL query with domain knowledge. On the challenging Text-to-SQL benchmark Spider, IRNet achieves 46.7% accuracy, obtaining 19.5% absolute improvement over previous state-of-the-art approaches. At the time of writing, IRNet achieves the first position on the Spider leaderboard.

Key facts. Cited by 210 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 May 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 43; 2022: 27; 2023: 44; 2024: 37; 2025: 35; 2026: 24.

210 papers.

Sort:

By area:

2023Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLscited by 667
2023DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correctioncited by 465
2023Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluationcited by 378
2023RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQLcited by 191
2024CodeS: Towards Building Open-source Language Models for Text-to-SQLcited by 183
2024CHESS: Contextual Harnessing for Efficient SQL Synthesiscited by 164
2023C3: Zero-shot Text-to-SQL with ChatGPTcited by 159
2023MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQLcited by 158
2021LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relationscited by 119
2024Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQLcited by 118
2021Towards Robustness of Text-to-SQL Models against Synonym Substitutioncited by 110
2021KaggleDBQA: Realistic Evaluation of Text-to-SQL Parserscited by 105

38 more

2024MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generationcited by 103
2023Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsingcited by 93
2021Exploring Underexplored Limitations of Cross-Domain Text-to-SQL Generalizationcited by 92
2022RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQLcited by 85
2023Is GPT-4 a Good Data Analyst?cited by 69
2024The Death of Schema Linking? Text-to-SQL in the Age of Well-Reasoned Language Modelscited by 67
2021Natural SQL: Making SQL Easier to Infer from Natural Language Specificationscited by 66
2025Reasoning-SQL: Reinforcement Learning with SQL Tailored Partial Rewards for Reasoning-Enhanced Text-to-SQLcited by 59
2023How to Prompt LLMs for Text-to-SQL: A Study in Zero-shot, Single-domain, and Cross-domain Settingscited by 57
2021Unlocking Compositional Generalization in Pre-trained Models Using Intermediate Representationscited by 55
2024A Survey of Text-to-SQL in the Era of LLMs: Where are we, and where are we going?cited by 54
2024Synthesizing Text-to-SQL Data from Weak and Strong LLMscited by 53
2022A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directionscited by 51
2024RSL-SQL: Robust Schema Linking in Text-to-SQL Generationcited by 51
2025OpenSearch-SQL: Enhancing Text-to-SQL with Dynamic Few-shot and Consistency Alignmentcited by 51
2022Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expectcited by 47
2023Skill-Based Few-Shot Selection for In-Context Learningcited by 46
2024DTS-SQL: Decomposed Text-to-SQL with Small Large Language Modelscited by 45
2021SADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQLcited by 43
2023SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (extended)cited by 42
2023ScienceBenchmark: A Complex Real-World Benchmark for Evaluating Natural Language to SQL Systemscited by 42
2021Learning to Synthesize Data for Semantic Parsingcited by 41
2022S2SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parserscited by 41
2022Is a Question Decomposition Unit All We Need?cited by 37
2021ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parsercited by 35
2025LLM-JEPA: Large Language Models Meet Joint Embedding Predictive Architecturescited by 35
2024PURPLE: Making a Large Language Model a Better SQL Writercited by 34
2024MAGIC: Generating Self-Correction Guideline for In-Context Text-to-SQLcited by 34
2021NL-EDIT: Correcting semantic parse errors through natural language interactioncited by 33
2021Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsingcited by 32
2024Decomposition for Enhancing Attention: Improving LLM-based Text-to-SQL through Workflow Paradigmcited by 31
2022Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsingcited by 30
2024A Survey of Table Reasoning with Large Language Modelscited by 29
2021Awakening Latent Grounding from Pretrained Language Models for Semantic Parsingcited by 28
2022Importance of Synthesizing High-quality Data for Text-to-SQL Parsingcited by 27
2023Natural Language Interfaces for Tabular Data Querying and Visualization: A Surveycited by 27
2022HIE-SQL: History Information Enhanced Network for Context-Dependent Text-to-SQL Semantic Parsingcited by 26
2025LinkAlign: Scalable Schema Linking for Real-World Large-Scale Multi-Database Text-to-SQLcited by 26

50 most cited of 210 All 210 (JSON)