Abstract:
Text-to-SQL is a critical task in natural language processing that converts natural language queries into SQL commands, enabling non-technical users to interact with databases. Early rule-based methods evolved into machine learning models, with recent advancements leveraging large language models (LLMs) to improve query understanding and generation. Despite signif icant progress, challenges like schema linking and logical reasoning persist. This paper reviews current approaches, highlights key models, and discusses emerging trends, particularly in en hancing model performance through techniques like synthetic data generation and task-aligned schema linking.
