SQL-Exchange: Transforming SQL Queries Across Domains
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866908845090013184 |
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| author | Daviran, Mohammadreza Lin, Brian Rafiei, Davood |
| author_facet | Daviran, Mohammadreza Lin, Brian Rafiei, Davood |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_07087 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SQL-Exchange: Transforming SQL Queries Across Domains Daviran, Mohammadreza Lin, Brian Rafiei, Davood Databases Artificial Intelligence Computation and Language 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. |
| title | SQL-Exchange: Transforming SQL Queries Across Domains |
| topic | Databases Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2508.07087 |