Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQL

Understand

Generating accurate SQL from users' natural language questions (text-to-SQL) remains a long-standing challenge due to the complexities involved in user question understanding, database schema comprehension, and SQL generation.

• Traditional text-to-SQL systems, which combine human engineering and deep neural networks, have made significant progress.
• Subsequently, pre-trained language models (PLMs) have been developed for text-to-SQL tasks, achieving promising results.
• However, as modern databases and user questions grow more complex, PLMs with a limited parameter size often produce incorrect SQL.