Text to SQL
Text-to-SQL research aims to automatically translate natural language queries into structured SQL queries, enabling non-experts to access and analyze data in relational databases. Current research heavily utilizes large language models (LLMs), focusing on improving accuracy through techniques like chain-of-thought prompting, multi-agent systems, and data augmentation strategies to address challenges such as complex schemas and ambiguous queries. This field is significant because it democratizes data access, impacting various sectors by streamlining data analysis and reducing the reliance on specialized SQL knowledge. Furthermore, ongoing work addresses the need for more robust evaluation metrics and benchmarks that reflect real-world complexities, particularly within enterprise settings.
Papers
Valid Text-to-SQL Generation with Unification-based DeepStochLog
Ying Jiao, Luc De Raedt, Giuseppe MarraKU Leuven●Orebro UniversityTinySQL: A Progressive Text-to-SQL Dataset for Mechanistic Interpretability Research
Philip Quirke, Clement Neo, Abir Harrasse, Dhruv Nathawani, Amir AbdullahMartian●Apart Research●Gretel.ai●Cynch.ai
STaR-SQL: Self-Taught Reasoner for Text-to-SQL
Mingqian He, Yongliang Shen, Wenqi Zhang, Qiuying Peng, Jun Wang, Weiming LuZhejiang University●OPPO Research InstituteOpenSearch-SQL: Enhancing Text-to-SQL with Dynamic Few-shot and Consistency Alignment
Xiangjin Xie, Guangwei Xu, Lingyan Zhao, Ruijie Guo
SQL-o1: A Self-Reward Heuristic Dynamic Search Method for Text-to-SQL
Shuai Lyu, Haoran Luo, Zhonghong Ou, Yifan Zhu, Xiaoran Shang, Yang Qin, Meina SongUncovering the Impact of Chain-of-Thought Reasoning for Direct Preference Optimization: Lessons from Text-to-SQL
Hanbing Liu, Haoyang Li, Xiaokang Zhang, Ruotong Chen, Haiyong Xu, Tian Tian, Qi Qi, Jing ZhangSAFE-SQL: Self-Augmented In-Context Learning with Fine-grained Example Selection for Text-to-SQL
Jimin Lee, Ingeol Baek, Byeongjeong Kim, Hwanhee Lee