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This paper introduces Schema-Aware Localisation (SAL), a lightweight middleware that grounds LLM-generated SQL queries in live Oracle schema catalog to eliminate hallucination errors, achieving 62.6% execution-grounded truth on TPC-H questions without manual schema curation.
This paper systematically analyzes multiple pipeline extension components for Natural Language to SQL translation, including intermediate representations, synthetic data, preprocessing, and a reranker, using SmBoP and RASAT architectures, and finds that their interactions matter more than simply combining all components.
This paper presents a semantic-layer-mediated NL2SQL agent that decouples intent from physical execution by reasoning over a curated semantic model, achieving 94.15% execution accuracy on the Spider2-snow benchmark.
Soma-SQL proposes an autonomous method to resolve multi-source ambiguity in natural language to SQL translation using synthetic query logs and ambiguity-driven execution probing, achieving 13% improvement in execution accuracy over state-of-the-art baselines.
This paper presents AgentNLQ, a multi-agent system for natural language to SQL conversion that achieves 78.1% semantic accuracy on the BIRD benchmark through schema enrichment and a self-correcting orchestrator.
FD-NL2SQL is a feedback-driven natural language to SQL system for clinical oncology databases that improves with use through clinician edits and logic-based SQL augmentation. The system decomposes natural language questions into predicates, retrieves expert-verified exemplars, and synthesizes executable SQL with continuous learning capabilities.
ROSE is a novel intent-centered evaluation metric for NL2SQL that uses a Prover-Refuter cascade to assess semantic correctness independently of ground-truth SQL, achieving 24% better agreement with human experts than existing metrics. The paper addresses limitations of Execution Accuracy and provides a re-evaluation of 19 NL2SQL methods with publicly released resources.