AgentNLQ: A General-Purpose Agent for Natural Language to SQL
Natural language to SQL (NL2SQL) conversion is an important problem for researchers and enterprises due to the ubiquitous importance of relational databases in broad-ranging practical problems. Despite the rapid advancements in the capabilities of LLMs, NL2SQL has not reached parity in accuracy with human expert SQL writers, hence needing additional improvements in NL2SQL algorithms. This study presents a new multi-agent method for NL2SQL that achieves 78.1% semantic accuracy on the BIg Bench for LaRge-scale Database (BIRD) benchmark. Our method leverages a semantically enriched representation of user-provided schema, adds user-provided business rules, and produces accurate SQL queries. T...
| Authors | Olena Bogdanov, Yeunji Jung, Chandra Dhir et al. |
|---|---|
| Primary category | cs.AI |
| Published | 18 May 2026 |
| Last updated at source | 18 May 2026 |
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