SQL-Exchange: Transforming SQL Queries Across Domains
Not on the map. Why?
Abstract (arXiv)
We introduce SQL-Exchange, a framework for mapping SQL queries across different database schemas by preserving the source query structure while adapting domain-specific elements to align with the target schema. We investigate the conditions under which such mappings are feasible and beneficial, and examine their impact on enhancing the in-context learning performance of text-to-SQL systems as a downstream task. Our comprehensive evaluation across multiple model families and benchmark datasets -- assessing structural alignment with source queries, execution validity on target databases, and semantic correctness -- demonstrates that SQL-Exchange is effective across a wide range of schemas and query types. Our results further show that both in-context prompting with mapped queries and fine-tuning on mapped data consistently yield higher text-to-SQL performance than using examples drawn directly from the source schema.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 24 of its 49 references that are in the map. First posted on arXiv 9 Aug 2025.
Builds on
24 of 49 references.
References · 24
- 2024Gemini 1.5: Unlocking multimodal understanding across millions of tokens of contextcited by 3,224
- 2024Qwen2.5-Coder Technical Reportcited by 1,203
- 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,139
- 2023Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLscited by 688
- 2023DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correctioncited by 477
- 2023Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluationcited by 385
- 2023RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQLcited by 195
- 2021GPT3Mix: Leveraging Large-scale Language Models for Text Augmentationcited by 187
- 2023AugGPT: Leveraging ChatGPT for Text Data Augmentationcited by 130
- 2023Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitationscited by 100
- 2025OmniSQL: Synthesizing High-quality Text-to-SQL Data at Scalecited by 91
- 2025Synthetic Data Generation Using Large Language Models: Advances in Text and Codecited by 61
12 more
- 2024DTS-SQL: Decomposed Text-to-SQL with Small Large Language Modelscited by 45
- 2021Learning to Synthesize Data for Semantic Parsingcited by 41
- 2021Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsingcited by 33
- 2018Question Generation from SQL Queries Improves Neural Semantic Parsingcited by 29
- 2022Importance of Synthesizing High-quality Data for Text-to-SQL Parsingcited by 28
- 2024Source2Synth: Synthetic Data Generation and Curation Grounded in Real Data Sourcescited by 28
- 2023Prompting Large Language Models with Chain-of-Thought for Few-Shot Knowledge Base Question Generationcited by 21
- 2025SQLForge: Synthesizing Reliable and Diverse Data to Enhance Text-to-SQL Reasoning in LLMscited by 11
- 2024MoMQ: Mixture-of-Experts Enhances Multi-Dialect Query Generation across Relational and Non-Relational Databasescited by 6
- 2023Diversity Enhanced Narrative Question Generation for Storybookscited by 5
- 2024Translating between SQL Dialects for Cloud Migrationcited by 4
- 2024TabulaX: Leveraging Large Language Models for Multi-Class Table Transformationscited by 3
Built on by
Citing papers per year: 2026: 1.
1 paper.