{"ID":2884466,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.07087","arxiv_id":"2508.07087","title":"SQL-Exchange: Transforming SQL Queries Across Domains","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2508.07087","url_pdf":"https://arxiv.org/pdf/2508.07087v2","authors":"[\"Mohammadreza Daviran\",\"Brian Lin\",\"Davood Rafiei\"]","published":"2025-08-09T19:55:54Z","proceeding":"cs.DB","tasks":"[\"cs.DB\",\"cs.AI\",\"cs.CL\"]","methods":"[]","has_code":false}