The field of Text-to-SQL and database optimization is witnessing significant advancements with a focus on improving performance, efficiency, and scalability. Researchers are exploring innovative approaches to address the challenges of schema linking, query generation, and database testing. Notably, the use of large language models and chain-of-thought reasoning is being investigated to enhance the accuracy and effectiveness of Text-to-SQL systems. Furthermore, there is a growing emphasis on developing cost-efficient and scalable solutions that can be deployed in real-world scenarios.

Some noteworthy papers in this area include: Feather-SQL, which introduces a lightweight framework for natural language to SQL tasks using small language models. LinkAlign, which proposes a novel framework for scalable schema linking in real-world large-scale multi-database scenarios. ExCoT, which presents a framework that optimizes reasoning for Text-to-SQL with execution feedback, achieving state-of-the-art performance on benchmark datasets. EllieSQL, which proposes a complexity-aware routing framework for cost-efficient Text-to-SQL, reducing token use by over 40% without compromising performance.