Text to SQL

Text-to-SQL research aims to automatically translate natural language queries into structured SQL queries, enabling non-experts to access and analyze data in relational databases. Current research heavily utilizes large language models (LLMs), focusing on improving accuracy through techniques like chain-of-thought prompting, multi-agent systems, and data augmentation strategies to address challenges such as complex schemas and ambiguous queries. This field is significant because it democratizes data access, impacting various sectors by streamlining data analysis and reducing the reliance on specialized SQL knowledge. Furthermore, ongoing work addresses the need for more robust evaluation metrics and benchmarks that reflect real-world complexities, particularly within enterprise settings.