The field of natural language processing (NLP) has made significant progress, especially in natural language to SQL (NL2SQL) technology. There is a trade-off between accuracy and adaptability in the traditional NL2SQL method, and it is difficult to meet the needs of different databases and complex queries. This article will introduce the XiYan-SQL framework launched by the Alibaba research team, how this framework solves these challenges through innovative methods and significantly improves the performance of NL2SQL.
Natural language to SQL (NL2SQL) technology is developing rapidly and has become an important innovation in the field of natural language processing (NLP). This technology enables users to convert natural language queries into Structured Query Language (SQL) statements. This advancement greatly facilitates the interaction between users who lack technical background and complex databases to obtain valuable information. NL2SQL technology not only opens new doors for large database exploration in various industries, but also improves work efficiency and decision-making capabilities.
However, there is a certain trade-off between query accuracy and adaptability during the implementation of NL2SQL. Some methods cannot guarantee accuracy when generating SQL queries, and are difficult to adapt to different types of databases. Some existing solutions rely on large language models (LLMs) to generate multiple outputs and select the best query through prompt engineering, but this approach increases the computational burden and is not suitable for real-time applications. At the same time, although supervised fine-tuning (SFT) can achieve targeted SQL generation, it faces difficulties in cross-domain applications and complex database operations, so innovative frameworks are urgently needed.
Alibaba's research team launched XiYan-SQL, a breakthrough NL2SQL framework. It incorporates a multi-generator ensemble strategy that combines the advantages of prompt engineering and SFT. A key innovation of XiYan-SQL is the introduction of M-Schema, a semi-structured schema representation method that can enhance the system's understanding of the database hierarchy, including data types, primary keys and sample values, thereby improving the accuracy and The ability to contextually fit SQL queries.
XiYan-SQL uses a three-stage process to generate and optimize SQL queries.
First, the system identifies relevant database elements through architectural links, thereby reducing redundant information and focusing on key structures. Next, SQL candidates are generated using generators based on example learning (ICL) and SFT. Finally, the system uses error correction models and selection models to optimize and filter the generated SQL to ensure that the best query is selected. XiYan-SQL integrates these steps into an efficient pipeline that goes beyond traditional methods.
After rigorous benchmark testing, XiYan-SQL performed well in multiple standard test sets. For example, it achieved an execution accuracy of 89.65% in the Spider test set, significantly ahead of previous top models.
In addition, XiYan-SQL also achieved excellent results in terms of adaptability to non-relational data sets, reaching an accuracy of 41.20% in the NL2GQL test set. These results demonstrate that XiYan-SQL has excellent flexibility and accuracy in a variety of scenarios.
github:https://github.com/XGenerationLab/XiYan-SQL
Highlight:
Innovative architecture representation: M-Schema enhances the understanding of the database hierarchy and improves query accuracy.
Advanced candidate generation: XiYan-SQL uses multiple generators to generate diverse SQL candidates, improving query quality.
Superior adaptability: Through benchmark tests, XiYan-SQL has demonstrated its excellent performance in a variety of databases, setting a new NL2SQL framework standard.
All in all, XiYan-SQL, as an advanced NL2SQL framework, has made significant breakthroughs in accuracy and adaptability through its innovative M-Schema schema representation, multi-generator integration strategy, and efficient optimization process, providing a basis for improving databases. Provides powerful tools for interactive efficiency and simplified user operations. Its GitHub link facilitates developers to further understand and use the framework.