arXiv:2605.21792v1 Announce Type: cross Abstract: Text-to-SQL ensembles improve over single-candidate generation by drawing multiple SQL candidates and selecting one, but their effectiveness is bounded by Pass@K, the probability that at least one of K candidates is correct. Existing methods source diversity heuristically through stochastic decoding or prompt variants, leaving candidate sets dominated by correlated failures. We present DivSkill-SQL, a residual skill optimization framework that builds complementary agentic Text-to-SQL ensembles without model fine-tuning: each new skill is optimi
The proliferation of Large Language Models (LLMs) and the increasing demand for automating complex data interactions are driving innovation in Text-to-SQL solutions, necessitating improved reliability and performance.
This research enhances the reliability and effectiveness of Text-to-SQL systems, which are crucial components for enterprise data access, BI automation, and the development of more capable AI agents.
The ability to build more robust and complementary Text-to-SQL ensembles without fine-tuning individual models changes the approach to achieving higher accuracy in natural language interfaces for databases.
- · AI Agent Developers
- · Enterprises with complex databases
- · Data Analysts
- · Database Management Systems
- · Manual SQL coders for routine tasks
Improved Text-to-SQL accuracy leads to more reliable automated data querying and reporting.
Enhanced data accessibility via natural language could accelerate enterprise digital transformation and AI integration.
More sophisticated and reliable Text-to-SQL capabilities could reduce the barrier to entry for non-technical users interacting with complex data systems, fostering broader innovation.
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Read at arXiv cs.LG