SHARE: An SLM-based Hierarchical Action CorREction Assistant for
  Text-to-SQL
Authors: name: Ge Qu, name: Jinyang Li, name: Bowen Qin, name: Xiaolong Li, name: Nan Huo, name: Chenhao Ma, name: Reynold Cheng

Current self-correction approaches in text-to-SQL face two critical
limitations: 1) Conventional self-correction methods rely on recursive
self-calls of LLMs, resulting in multiplicative computational overhead, and 2)
LLMs struggle to implement effective error detection and correction for
declarative SQL queries, as they fail to demonstrate the underlying reasoning
path. In this work, we propose SHARE, an SLM-based Hierarchical Action
corREction assistant that enables LLMs to perform more precise error
localization and efficient correction. SHARE orchestrates three specialized
Small Language Models (SLMs) in a sequential pipeline, where it first
transforms declarative SQL queries into stepwise action trajectories that
reveal underlying reasoning, followed by a two-phase granular refinement. We
further propose a novel hierarchical self-evolution strategy for data-efficient
training. Experimental results demonstrate that SHARE effectively enhances
self-correction capabilities while proving robust across various LLMs.
Furthermore, our comprehensive analysis shows that SHARE maintains strong
performance even in low-resource training settings, which is particularly
valuable for text-to-SQL applications with data privacy constraints.