arXiv:2603.03742v2 Announce Type: replace Abstract: Despite the remarkable performance of large language models (LLMs) in text-to-SQL (SQL generation), correctly producing SQL queries remains challenging during initial generation. The SQL refinement task is subsequently introduced to correct syntactic and semantic errors in generated SQL queries. However, existing paradigms face two major limitations: (i) self-debugging becomes increasingly ineffective as modern LLMs rarely produce explicit execution errors that can trigger debugging signals; (ii) self-correction exhibits low detection precisi
The increasing reliance on LLMs for code generation, specifically SQL, brings the challenge of error correction to the forefront, necessitating more sophisticated refinement mechanisms.
Improved SQL generation and refinement directly impacts data-driven decision-making, efficiency in enterprise software, and the broader utility of AI agents interacting with databases.
The ability to accurately model and correct SQL errors using LLMs marks a significant improvement over previous self-debugging methods, making AI-generated SQL more reliable and autonomous.
- · AI developers
- · Data engineers
- · Enterprises leveraging AI for data ops
- · SaaS platforms with AI code generation
- · Manual SQL debugging
- · Inefficient data querying processes
More robust and autonomous AI systems capable of generating and self-correcting database interactions.
Accelerated development of AI agents that can independently manage and query complex data environments without human oversight.
Increased automation in data science and business intelligence, potentially changing the skill requirements for database administrators and data analysts.
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Read at arXiv cs.CL