SQL Generation

SQL generation, or translating natural language queries into SQL code, aims to democratize data access by enabling non-experts to interact with databases using natural language. Current research focuses on improving the accuracy and reliability of large language models (LLMs) for this task, exploring techniques like multi-agent systems, iterative refinement, and the use of synthetic data to address limitations in existing datasets and model architectures. This field is significant because it has the potential to greatly improve data accessibility and efficiency across various domains, from business intelligence to scientific research, by bridging the gap between human language and database querying.