Generating accurate Structured Querying Language (SQL) is a long-standing
problem, especially in matching users' semantic queries with structured
databases and then generating structured SQL. Existing models typically input
queries and database schemas into the LLM and rely on the LLM to perform
semantic-structure matching and generate structured SQL. However, such
solutions overlook the structural information within user queries and
databases, which can be utilized to enhance the generation of structured SQL.
This oversight can lead to inaccurate or unexecutable SQL generation. To fully
exploit the structure, we propose a structure-to-SQL framework, which leverages
the inherent structure information to improve the SQL generation of LLMs.
Specifically, we introduce our Structure Guided SQL~(SGU-SQL) generation model.
SGU-SQL first links user queries and databases in a structure-enhanced manner.
It then decomposes complicated linked structures with grammar trees to guide
the LLM to generate the SQL step by step. Extensive experiments on two
benchmark datasets illustrate that SGU-SQL can outperform sixteen SQL
generation baselines.

利用用户查询和数据库的结构信息改进结构化 SQL 的生成问题，提出了一种结构引导的 SQL（SGU-SQL）生成模型，并通过实验验证其优于 16 种 SQL 生成方法。

结构引导的大型语言模型用于 SQL 生成

Structure Guided Large Language Model for SQL Generation

The availability of both structured and unstructured databases, such as
electronic health data, social media data, patent data, and surveys that are
often updated in real time, among others, has grown rapidly over the past
decade. With this expansion, the statistical and methodological questions
around data integration, or rather merging multiple data sources, has also
grown. Specifically, the science of the ``data cleaning pipeline'' contains
four stages that allow an analyst to perform downstream tasks, predictive
analyses, or statistical analyses on ``cleaned data.'' This article provides a
review of this emerging field, introducing technical terminology and commonly
used methods.

通过介绍技术术语和常用方法，综述了数据集成和数据清洗流程中的四个阶段，探讨了在现有的结构化和非结构化数据库中进行预测分析或统计分析所涉及的统计和方法学问题。

数据清洗流程初探

A Primer on the Data Cleaning Pipeline

The current state-of-the-art generative models for open-domain question
answering (ODQA) have focused on generating direct answers from unstructured
textual information. However, a large amount of world's knowledge is stored in
structured databases, and need to be accessed using query languages such as
SQL. Furthermore, query languages can answer questions that require complex
reasoning, as well as offering full explainability. In this paper, we propose a
hybrid framework that takes both textual and tabular evidence as input and
generates either direct answers or SQL queries depending on which form could
better answer the question. The generated SQL queries can then be executed on
the associated databases to obtain the final answers. To the best of our
knowledge, this is the first paper that applies Text2SQL to ODQA tasks.
Empirically, we demonstrate that on several ODQA datasets, the hybrid methods
consistently outperforms the baseline models that only take homogeneous input
by a large margin. Specifically we achieve state-of-the-art performance on
OpenSQuAD dataset using a T5-base model. In a detailed analysis, we demonstrate
that the being able to generate structural SQL queries can always bring gains,
especially for those questions that requires complex reasoning.

本文提出一种混合型框架，将文本和表格式证据作为输入，根据问题生成直接答案或 SQL 查询，并在几个开放领域问题回答（ODQA）数据集上表现出了明显优于基线模型的表现。在详细的分析中，证明生成结构化 SQL 查询将始终带来收益，尤其是对于那些需要复杂推理的问题。这是第一个将 Text2SQL 应用于 ODQA 任务的论文。