The Nuts and Bolts of Natural Language to SQL Translation: A Systematic Analysis of Model Pipeline Optimisation Approaches and their Interactions

2026-07-12

AI Summary

This study addresses the persistent challenges of accuracy and robustness in natural language to SQL (NL2SQL) translation under complex query scenarios. The authors systematically evaluate the combined effects of multiple optimization strategies—including the NatSQL intermediate representation, synthetic data preprocessing and fine-tuning, and a novel SQL re-ranking model—using SmBoP and RASAT as backbone architectures. Through ablation studies and Shapley value analysis, they quantitatively assess, for the first time, the interaction effects among these components, revealing that their performance gains are not merely additive. The results demonstrate that non-trivial combinations of these techniques yield significant improvements on benchmarks such as Spider, underscoring the critical role of synergistic interactions among system components.

Abstract

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.

Problem

• Natural Language to SQL
• NL2SQL
• model pipeline
• translation
• large language models

Innovation

• NL2SQL
• pipeline optimization
• NatSQL
• reranker
• Shapley analysis