Text to SQL Parser

Text-to-SQL parsing aims to automatically translate natural language questions into executable SQL queries, enabling users to access database information without SQL expertise. Current research focuses on improving the accuracy and robustness of these parsers, particularly for complex queries involving multiple tables and handling ambiguous or unanswerable questions, often employing large language models (LLMs) and graph-based neural networks within seq2seq architectures. These advancements are crucial for bridging the gap between human-computer interaction and database access, impacting fields like data analytics and business intelligence by making data more readily available to a wider audience.