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调查强调了使用大型语言模型在文本到SQL中的进展Survey highlights advancements in Text-to-SQL using large language models
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以下内容为 AI 自动翻译，英文原文附后供参考。
摘要
最近在arXiv上发布的一项调查研究了文本到SQL系统的演变，强调了大型语言模型（LLMs）在改善自然语言到SQL查询翻译中的作用。该调查对不同的LLM驱动的方法进行了分类，并讨论了相关挑战，包括随着这些技术的发展对负责任的人工智能实践的需求。这一全面的分析阐明了文本到SQL系统的当前状态和未来方向，为研究人员和从业者提供了宝贵的见解。
A recent survey published on arXiv examines the evolution of Text-to-SQL systems, emphasizing the role of large language models (LLMs) in improving the translation of natural language into SQL queries. The survey categorizes different approaches driven by LLMs and discusses the associated challenges, including the need for responsible AI practices as these technologies develop. This comprehensive analysis sheds light on the current state and future directions of Text-to-SQL systems, providing valuable insights for researchers and practitioners alike.
要点
大型语言模型显著增强了文本到SQL的翻译能力。
Large language models significantly enhance Text-to-SQL translation capabilities.
调查对各种LLM驱动的文本到SQL方法进行了分类。
The survey categorizes various LLM-driven approaches to Text-to-SQL.
随着文本到SQL系统的发展，负责任的人工智能实践至关重要。
Responsible AI practices are crucial as Text-to-SQL systems evolve.
为何重要
理解文本到SQL系统的进展对于利用人工智能进行数据管理和查询至关重要。这项研究突显了LLMs在自然语言与数据库交互之间架起桥梁的潜力。
Understanding the advancements in Text-to-SQL systems is essential for leveraging AI in data management and querying. This research highlights the potential of LLMs to bridge the gap between natural language and database interactions.
产品经理
产品经理应关注这些进展，以更好地将产品特性与用户需求对齐，特别是在通过自然语言接口使数据查询更易于访问方面。
Product managers should be aware of these advancements to better align product features with user needs, particularly in making data querying more accessible through natural language interfaces.
开发者
对于开发者而言，这项调查提供了实施文本到SQL系统的最新方法论和挑战的见解，指导他们采用最佳实践和创新方法。
For developers, this survey provides insights into the latest methodologies and challenges in implementing Text-to-SQL systems, guiding them in adopting best practices and innovative approaches.
学生与求职者
学生和求职者可以通过理解这些进展受益，因为在LLMs和文本到SQL系统方面的专业知识可以提高他们在数据科学和人工智能相关领域的就业能力。
Students and job-seekers can benefit from understanding these advancements, as expertise in LLMs and Text-to-SQL systems can enhance their employability in data science and AI-related fields.
覆盖来源
来源标题与摘录保留原文，以便核对与追溯。
arXiv cs.AI
Reimagining Text-to-SQL: A Survey of Large Language Model Architectures and StrategiesRecent advancements in Text-to-SQL systems have been driven by large language models (LLMs), which enhance the translation of natural language into SQL queries. The article categorizes various LLM-driven approaches and addresses the challenges and responsible AI practices necessary as these systems evolve.