Text-to-SQL in the Era of Large Language Models: A Survey of Reasoning-Centric and Efficiency-Centric Approaches

Text-to-SQL translates natural-language questions into executable SQL, enabling non-expert access to relational databases. Despite advances in large language models (LLMs), reliable systems must ground questions in schemas and values, handle large schemas, and preserve semantic correctness. This survey reviews the evolution toward LLM-based and agentic systems through an objective-oriented taxonomy of Reasoning-Centric (RC) and Efficiency-Centric (EC) approaches. RC emphasizes interpretation, grounding, workflow organization, candidate search, and verification; EC emphasizes acquiring, adapting, transferring, and deploying capabilities under identifiable resource or deployment constraints. Assignments reflect a study’s stated objectives and main contribution, not model size, openness, or training strategy alone. We connect three recurring challenges to ten methodological families and provide an evidence-linked inventory of 114 studies with eleven boundary interpretations. Benchmark-aware comparisons retain model, split, metric, and inference-budget distinctions, including twelve illustrative BIRD development configurations from ten studies. Explicit token values are reported by 31 of 114 studies; a further 6 additional approximate, range, or bound disclosures bring the total to 37, including 16 of the 50 representative studies. Heterogeneous scopes, spanning prompt context, pipeline components, dataset totals, and offline generation, limit direct comparison of end-to-end inference consumption. These findings motivate configuration-specific resource reporting. We also discuss security and safety risks and their evaluation implications. The taxonomy, comparisons, and reporting framework support joint analysis of reasoning quality and resource use in systems combining RC and EC mechanisms.