Schema linking is a crucial step in Text-to-SQL pipelines. Its goal is to retrieve the relevant tables and columns of a target database for a user's query while disregarding irrelevant ones. However, imperfect schema linking can often exclude required columns needed for accurate query generation. In this work, we revisit schema linking when using the latest generation of large language models (LLMs). We find empirically that newer models are adept at utilizing relevant schema elements during generation even in the presence of large numbers of irrelevant ones. As such, our Text-to-SQL pipeline entirely forgoes schema linking in cases where the schema fits within the model's context window in order to minimize issues due to filtering required schema elements. Furthermore, instead of filtering contextual information, we highlight techniques such as augmentation, selection, and correction, and adopt them to improve the accuracy of our Text-to-SQL pipeline. Our approach ranks first on the BIRD benchmark achieving an accuracy of 71.83%.
DCG-SQL: Enhancing In-Context Learning for Text-to-SQL with Deep Contextual Schema Link Graph
Department of Artificial Intelligence, Sungkyunkwan University（人工智能系，成均馆大学）；Department of Immersive Media Engineering, Sungkyunkwan University（沉浸媒体工程系，成均馆大学）；Department of Computer Science and Engineering, Sungkyunkwan University（计算机科学与工程系，成均馆大学）
PET-SQL: A Prompt-Enhanced Two-Round Refinement of Text-to-SQL with Cross-consistency
SenseTime Research（商汤科技研究院）；Peking University（北京大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机学院）；University of Cologne（科隆大学）
Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsing
University of Chinese Academy of Sciences（中国科学院大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Alibaba Group（阿里巴巴集团）；Massachusetts Institute of Technology（麻省理工学院）
Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents
School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）；Big Data Shortage and Management, MIIT（工业和信息化部大数据短缺与管理局）；School of Computing, National University of Singapore（新加坡国立大学计算机学院）；School of Computing and Information Systems, Singapore Management University（新加坡管理大学计算与信息系统学院）
ProSPy: A Profiling-Driven SQL-Python Agentic Framework for Enterprise Text-to-SQL
State Key Lab of CAD&CG（计算机辅助设计与图形学国家重点实验室）；School of Software Technology（软件技术学院）；Tencent TEG（腾讯科技集团）；School of Mathematical Sciences, Peking University（北京大学数学科学学院）；Zhejiang University（浙江大学）
DBCopilot: Natural Language Querying over Massive Databases via Schema Routing
Institute of Software, CAS（中国科学院软件研究所）；Hangzhou Institute for Advanced Study, UCAS（中国科学院大学杭州高等研究院）；University of Chinese Academy of Sciences (UCAS)（中国科学院大学）；Alibaba Cloud Intelligence Group（阿里巴巴云智能集团）
Enhancing Text-to-SQL Capabilities of Large Language Models via Domain Database Knowledge Injection
School of Cyber Science Engineering, Huazhong University of Science and Technology（华中科技大学网络空间科学与工程学院）；Wuhan AI Research Foundation Model Research Center（武汉人工智能研究基金会大模型研究中心）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Peng Cheng Laboratory（鹏城实验室）
From Natural Language to SQL: Review of LLM-based Text-to-SQL Systems
School of Computing and Informatics, University of Louisiana at Lafayette（路易斯安那大学拉斐特分校计算与信息学院）；School of Mechanical Engineering, University of Louisiana at Lafayette（路易斯安那大学拉斐特分校机械工程学院）
