title: SHARE: An SLM-based Hierarchical Action CorREction Assistant for
    Text-to-SQL
summary: 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.
paper_authors._id: 683fd0aca44e6cbbe3c185b2
paper_authors.name: Ge Qu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-04T08:54:33.919Z
paper_authors._id: 6419435385030eca6ac94701
paper_authors.avatarUrl: /avatars/c49ff1991739de49ec98c8310ab21e46.svg
paper_authors.fullname: Ge Qu
paper_authors.type: user
paper_authors.user: gq2138
paper_authors._id: 683fd0aca44e6cbbe3c185b3
paper_authors.name: Jinyang Li
paper_authors._id: 683fd0aca44e6cbbe3c185b4
paper_authors.name: Bowen Qin
paper_authors._id: 683fd0aca44e6cbbe3c185b5
paper_authors.name: Xiaolong Li
paper_authors._id: 683fd0aca44e6cbbe3c185b6
paper_authors.name: Nan Huo
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-06T07:42:03.537Z
paper_authors._id: 60adfff0306d6873ec42d545
paper_authors.avatarUrl: /avatars/4a63f90638dbffebfeeee181a6d0220c.svg
paper_authors.fullname: Nan
paper_authors.type: user
paper_authors.user: NanHUO
paper_authors._id: 683fd0aca44e6cbbe3c185b7
paper_authors.name: Chenhao Ma
paper_authors._id: 683fd0aca44e6cbbe3c185b8
paper_authors.name: Reynold Cheng
paper_title: SHARE: An SLM-based Hierarchical Action CorREction Assistant for
    Text-to-SQL
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: self-correction LLMs SLM-based hierarchical action correction stepwise action trajectories granular refinement hierarchical self-evolution strategy data-efficient training