LitE-SQL: A Lightweight and Efficient Text-to-SQL Framework with Vector-based Schema Linking and Execution-Guided Self-Correction

• URL: http://arxiv.org/abs/2510.09014v1
• Date: Fri, 10 Oct 2025 05:27:47 GMT
• Title: LitE-SQL: A Lightweight and Efficient Text-to-SQL Framework with Vector-based Schema Linking and Execution-Guided Self-Correction
• Authors: Shengmin Piao, Jieun Lee, Sanghyun Park,
• Abstract summary: We introduce LitE-, a Lightweight and Efficient framework with two components.<n>On BIRD, LitE- achieves 72.10% execution accuracy, and on Spider it reaches 88.45%, demonstrating comparable or superior performance to Retriever.<n>Our findings demonstrate that high-quality Text-to-correction generation is feasible with lightweight models, offering a practical solution for privacy-sensitive and resource-constrained settings.
• Score: 5.123751486259634
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Text-to-SQL task translates natural language questions into SQL queries, enabling intuitive database interaction for non-experts. While recent methods leveraging Large Language Models (LLMs) achieve strong performance, their reliance on proprietary models raise concerns about deployment feasibility and data privacy. In this work, we introduce LitE-SQL, a Lightweight and Efficient framework with two components: (i) a Schema Retriever that performs efficient schema linking using a vector database of pre-computed schema embeddings, and (ii) a SQL Generator fine-tuned in two stages-supervised fine-tuning followed by execution-guided reinforcement-enabling self-correction without costly multi-candidate generation. On BIRD, LitE-SQL achieves 72.10% execution accuracy, and on Spider 1.0 it reaches 88.45%, demonstrating comparable or superior performance to LLM-based methods despite using 2x to 30x fewer parameters. Our findings demonstrate that high-quality Text-to-SQL generation is feasible with lightweight models, offering a practical solution for privacy-sensitive and resource-constrained settings.

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