Text-to-SQL

Text-to-SQL studies how to turn natural-language questions into database queries that retrieve the intended information. Systems link questions to tables, columns, and values, then generate SQL using language models, structured intermediate plans, retrieval, or schema-aware semantic metadata. Common approaches add query decomposition, execution feedback, verification, and iterative repair; training methods include fine-tuning on curated or synthetic examples and reinforcement learning with execution-based rewards. Benchmarks such as Spider and BIRD test execution accuracy, but real deployments also expose challenges in large or changing schemas, ambiguity, multiple SQL dialects, and balancing reliability against cost and latency.