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EMLC: An extensible multi-level correction framework for text-to-SQL
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DOI：10.1016/j.ipm.2025.104560.png)
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Abstract
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To address three key challenges of Text-to-SQL self-correction, including schema mismatch, structural incompleteness, and semantic validation weakness, this paper proposes EMLC, an extensible multi-level correction framework that hierarchically integrates schema, skeleton, and execution corrections. EMLC incorporates a dual-validation schema correction mechanism that combines large language model (LLM)-based prediction with token-level mapping for precise schema alignment. Moreover, it employs supervised fine-tuning skeleton generation to detect and correct keyword-level errors through abstract skeleton comparison, while the executability verification strategy is designed to further ensure both syntactic integrity and semantic fidelity of generated queries. EMLC supports plug-and-play integration with mainstream LLMs and flexible scalability. Experiments on the SPIDER and BIRD datasets show that EMLC achieves state-of-the-art execution accuracy, outperforming baseline methods by 2–4 %. Ablation studies further validate the individual contributions of each component and their synergistic effects.
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