综述大模型在文本转SQL中的进展与挑战，助力非技术用户轻松查询数据库。
Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunities
- 系统梳理大模型驱动的文本转SQL研究趋势与技术路径
- 总结主流数据集与评估指标，揭示当前性能瓶颈
- 指出可解释性、复杂查询等关键难题，适合研究者参考
将自然语言问题转换为SQL查询的文本转SQL技术，已成为帮助无SQL知识用户访问关系型数据库的关键工具。近年来大语言模型（LLMs）在自然语言处理领域的突破，为提升文本转SQL系统能力开辟了新路径。本研究对基于大模型的文本转SQL进行了系统性综述，重点分析四个方面：（1）基于大模型的文本转SQL研究趋势；（2）从多个维度深入剖析现有技术方法；（3）总结现有文本转SQL数据集与评估指标；（4）探讨该领域潜在挑战与未来发展方向。本文旨在为研究者提供对大模型文本转SQL的深入理解，激发该领域的创新与进步。
原文摘要 · Abstract (English)
Converting natural language (NL) questions into SQL queries, referred to as Text-to-SQL, has emerged as a pivotal technology for facilitating access to relational databases, especially for users without SQL knowledge. Recent progress in large language models (LLMs) has markedly propelled the field of natural language processing (NLP), opening new avenues to improve text-to-SQL systems. This study presents a systematic review of LLM-based text-to-SQL, focusing on four key aspects: (1) an analysis of the research trends in LLM-based text-to-SQL; (2) an in-depth analysis of existing LLM-based text-to-SQL techniques from diverse perspectives; (3) summarization of existing text-to-SQL datasets and evaluation metrics; and (4) discussion on potential obstacles and avenues for future exploration in this domain. This survey seeks to furnish researchers with an in-depth understanding of LLM-based text-to-SQL, sparking new innovations and advancements in this field.
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