SHARE: An SLM-based Hierarchical Action CorREction Assistant for
  Text-to-SQL
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.
683fd0aca44e6cbbe3c185b2
Ge Qu
claimed_verified
2025-06-04T08:54:33.919Z
6419435385030eca6ac94701
/avatars/c49ff1991739de49ec98c8310ab21e46.svg
Ge Qu
user
gq2138
683fd0aca44e6cbbe3c185b3
Jinyang Li
683fd0aca44e6cbbe3c185b4
Bowen Qin
683fd0aca44e6cbbe3c185b5
Xiaolong Li
claimed_verified
2025-06-25T08:09:44.486Z
653693cb8ee17cfd44eed8ce
/avatars/82be2428bec4e06c0a15a27647b9b8aa.svg
Xiaolong Li
user
xia01ongLi
683fd0aca44e6cbbe3c185b6
Nan Huo
claimed_verified
2025-06-06T07:42:03.537Z
60adfff0306d6873ec42d545
/avatars/4a63f90638dbffebfeeee181a6d0220c.svg
Nan
user
NanHUO
683fd0aca44e6cbbe3c185b7
Chenhao Ma
683fd0aca44e6cbbe3c185b8
Reynold Cheng
SHARE: An SLM-based Hierarchical Action CorREction Assistant for
  Text-to-SQL
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.
SHARE, an SLM-based Hierarchical Action RECorrection system, enhances LLMs in text-to-SQL by transforming SQL queries into action trajectories and employing a granular refinement process, improving error detection and correction efficiency.
self-correction LLMs SLM-based hierarchical action correction stepwise action trajectories granular refinement hierarchical self-evolution strategy data-efficient training