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
LR-SQL: A Supervised Fine-Tuning Method for Text2SQL Tasks under Low-Resource Scenarios
Wen Wuzhenghong, Zhang Yongpan, Pan Su, Sun Yuwei, Lu Pengwei, Ding Cheng
Learning from Imperfect Data: Towards Efficient Knowledge Distillation of Autoregressive Language Models for Text-to-SQL
Qihuang Zhong, Kunfeng Chen, Liang Ding, Juhua Liu, Bo Du, Dacheng Tao
CHASE-SQL: Multi-Path Reasoning and Preference Optimized Candidate Selection in Text-to-SQL
Mohammadreza Pourreza, Hailong Li, Ruoxi Sun, Yeounoh Chung, Shayan Talaei, Gaurav Tarlok Kakkar, Yu Gan, Amin Saberi, Fatma Ozcan, Sercan O. Arik
Enhancing LLM Fine-tuning for Text-to-SQLs by SQL Quality Measurement
Shouvon Sarker, Xishuang Dong, Xiangfang Li, Lijun Qian
DataGpt-SQL-7B: An Open-Source Language Model for Text-to-SQL
Lixia Wu, Peng Li, Junhong Lou, Lei Fu
Enhancing Text-to-SQL Capabilities of Large Language Models via Domain Database Knowledge Injection
Xingyu Ma, Xin Tian, Lingxiang Wu, Xuepeng Wang, Xueming Tang, Jinqiao Wang
FLEX: Expert-level False-Less EXecution Metric for Reliable Text-to-SQL Benchmark
Heegyu Kim, Taeyang Jeon, Seunghwan Choi, Seungtaek Choi, Hyunsouk Cho