PTD-SQL：在Text-to-SQL中利用LLMs进行分区与针对性训练
PTD-SQL: Partitioning and Targeted Drilling with LLMs in Text-to-SQL
AI总结：
提出PTD-SQL方法，通过查询组分区和针对性训练提升LLMs在Text-to-SQL任务中的推理能力，在Spider和BIRD数据集上超越或匹敌SOTA。
摘要：
大型语言模型（LLMs）已成为Text-to-SQL任务的强大工具，展现出卓越的推理能力。与数学应用题和常识推理等任务不同，SQL解决方案具有相对固定的模式。这有助于研究LLMs是否能从分类思维中受益，类似于人类通过基于可比示例的归纳推理来获取知识。在本研究中，我们提出采用查询组分区使LLMs专注于学习特定于单一问题类型的思维过程，从而提升其在不同难度级别和问题类别上的推理能力。实验表明，多个先进的LLMs在配备PTD-SQL后，在Spider和BIRD数据集上能够超越或匹敌先前最先进（SOTA）的方法。有趣的是，初始性能不同的模型均展现出显著提升，主要在针对性训练后其能力边界处，这暗示了与人类进步的相似性。代码可在https://github.com/lrlbbzl/PTD-SQL获取。
英文摘要：
Large Language Models (LLMs) have emerged as powerful tools for Text-to-SQL tasks, exhibiting remarkable reasoning capabilities. Different from tasks such as math word problems and commonsense reasoning, SQL solutions have a relatively fixed pattern. This facilitates the investigation of whether LLMs can benefit from categorical thinking, mirroring how humans acquire knowledge through inductive reasoning based on comparable examples. In this study, we propose that employing query group partitioning allows LLMs to focus on learning the thought processes specific to a single problem type, consequently enhancing their reasoning abilities across diverse difficulty levels and problem categories. Our experiments reveal that multiple advanced LLMs, when equipped with PTD-SQL, can either surpass or match previous state-of-the-art (SOTA) methods on the Spider and BIRD datasets. Intriguingly, models with varying initial performances have exhibited significant improvements, mainly at the boundary of their capabilities after targeted drilling, suggesting a parallel with human progress. Code is available at https://github.com/lrlbbzl/PTD-SQL.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
