这篇论文把NL2SQL的优化组件拆开对比，告诉你NatSQL、微调、重排序器怎么搭配才最有效，不是堆砌就行。
该论文系统性分析了NL2SQL翻译流水线的多个扩展组件，包括NatSQL中间表示、预处理步骤、基于合成数据的微调以及新型重排序器。基于SmBoP和RASAT两个主干架构进行消融实验与Shapley分析。结果表明简单组合所有组件并不最优，组件效果依赖于与基线系统的交互。研究为轻量化模型开发提供了指导。
The Nuts and Bolts of Natural Language to SQL Translation: A Systematic Analysis of Model Pipeline Optimisation Approaches and their Interactions
In the age of large language models, Natural Language to SQL (NL2SQL) translation remains an open problem with many useful applications. We explore interactions between several NL2SQL pipeline extensions to inspire development of more lightweight models. Specifically, we integrate the NatSQL intermediate representation, include a preprocessing step and a fine-tuning step based on synthetic data, and develop a novel reranker model to improve SQL selection in the final beam. We perform an ablation study supplemented by a Shapley analysis of these different components integrated with two backbone architectures, SmBoP and RASAT. We find that simply combining all of them does not lead to best results, but that their impact depends on their interactions with the baseline system, as well as each other.