arXiv:2606.01246v1 Announce Type: new Abstract: Text-to-SQL on complex schemas is unreliable on a single pass, so recent systems generate multiple SQL candidates and let voting filter out errors. Yet voting alone is not enough, because the multi-candidate recipe has three coupled weaknesses: 1) sampling more from a single generator produces increasingly redundant candidates, 2) existing pipelines apply one generic correction to every non-clean execution result, while runtime errors, timeouts, and empty results each indicate a different distance from correctness, and 3) existing selectors rely
The increasing complexity of text-to-SQL tasks on intricate database schemas necessitates more robust and reliable methods beyond single-pass generation, driving innovation in error correction and multi-candidate approaches.
Improving the accuracy and reliability of Text-to-SQL systems unlocks greater accessibility to data for non-technical users and accelerates data analysis workflows, directly impacting decision-making speed and automation across industries.
This research introduces execution feedback as a primary mechanism to refine multi-candidate Text-to-SQL, providing more nuanced error correction than generic voting systems.
- · Data scientists
- · Business intelligence platforms
- · Enterprises with complex databases
- · AI agent developers
- · Inefficient data querying methods
- · Manual SQL coding for routine tasks
Enhanced text-to-SQL accuracy makes data access more democratic and efficient.
Broader adoption of sophisticated AI query tools could reduce the need for specialized data analysts for routine queries.
Increased data accessibility could lead to faster innovation cycles and more data-driven decision-making across entire organizations and sectors.
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Read at arXiv cs.AI