Domain Text to SQL

Domain Text-to-SQL research aims to automatically translate natural language questions into structured SQL queries for specific databases, enabling easier data access for non-programmers. Current efforts focus on improving the accuracy and reliability of these translations, particularly across diverse domains and database schemas, often leveraging large language models (LLMs) and techniques like prompting, ensemble methods, and multi-hop table retrieval to overcome challenges in understanding nuanced language and complex database structures. This field is significant because it bridges the gap between human-readable queries and machine-processable data, with potential applications ranging from efficient data analysis in healthcare to improved accessibility of information across various domains.