arXiv:2606.05906v1 Announce Type: new Abstract: Text-to-SQL maps natural language questions to executable SQL queries. Modern databases often contain large and complex schemas, making schema linking a critical step for accurate SQL generation. Existing methods either rely on full-schema generation, which leaves schema linking implicit within a large search space, or use a separate retriever trained with static gold-column supervision, whose targets may be suboptimal for the current generator policy. To address this issue, we propose Adaptive Co-optimization via Empirical Credit Assignment for
The increasing complexity of database schemas and the demand for more robust natural language interaction with data are driving innovation in Text-to-SQL models.
This development enhances the accuracy and adaptability of AI systems interacting with complex databases, making data access more efficient and accessible for non-technical users.
Existing Text-to-SQL methods are being improved with adaptive co-optimization, moving beyond static, potentially suboptimal supervision for schema linking.
- · AI developers
- · Data analytics platforms
- · Enterprises with large databases
- · End-users of data interaction tools
- · Legacy Text-to-SQL solutions
- · Manual SQL query writers
- · Companies reliant on less accurate data interaction
More accurate and efficient translation of natural language to SQL queries will lead to faster data retrieval and analysis.
Improved schema linking could enable AI agents to autonomously manage and query complex databases with greater reliability.
The widespread adoption of highly accurate Text-to-SQL could reduce the need for specialized SQL knowledge, democratizing data access broadly across industries.
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Read at arXiv cs.CL