Context Dependent Text to SQL

Context-dependent Text-to-SQL focuses on automatically translating multi-turn natural language questions into SQL database queries, aiming to improve the accuracy and efficiency of database interaction. Current research emphasizes enhancing large language models' ability to handle complex contextual information through techniques like improved prompting strategies, question rewriting, and data augmentation methods that generate diverse and high-quality training examples. These advancements are significant because they address the limitations of existing approaches, particularly in handling long conversations and cross-domain generalization, ultimately leading to more robust and user-friendly database interfaces.