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LR-SQL: A Supervised Fine-Tuning Method for Text2SQL Tasks under Low-Resource Scenarios

arXiv 2410.11457 ↗First posted 15 Oct 2024cs.DB cs.AI cs.CLcited by 0

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Abstract (arXiv)

Large language models revolutionize Text2SQL through supervised fine-tuning, yet a crucial limitation is overlooked: the complexity of databases leads to an increased context length, consequently resulting in higher GPU memory demands for model fine-tuning. To address this issue, we propose LR-SQL. LR-SQL comprises two supervised fine-tuning models: the schemalink model and the SQLgeneration model, with the schemalink model serving as the focal point for streamlining the overall process. During the fine-tuning of the schemalink model, LR-SQL breaks down the complete database into flexible combinations of tables with adjustable quantities, enabling the model to learn the relationships within the entire database from these dispersed slices. Furthermore, to enhance the model's ability to perceive the relationships among various discrete slices during inference, LR-SQL trains the model's Chain-of-Thought capability for this task. Experimental results demonstrate that LR-SQL can reduce the total GPU memory usage by 40% compared to existing fine-tuning methods, while only losing 2% of table prediction accuracy in schemalink task. For the overall Text2SQL task, the Execution Accuracy decrease by 0.6%.Our project is now available on https://github.com/hongWin/LR-SQL

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References · 27

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15 more
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  2. 2026Optimizing Small Language Models for NL2SQL via Chain-of-Thought Fine-Tuning
  3. 2026LIMIT: Less Is More for Instruction Tuning in Text-to-SQL
  4. 2024Open-SQL Framework: Enhancing Text-to-SQL on Open-source Large Language Models
  5. 2025Arctic-Text2SQL-R1: Simple Rewards, Strong Reasoning in Text-to-SQL
  6. 2026How Small Can You Go? A Controlled Study of LoRA Rank, Target Modules, and Quantization Trade-offs for Text-to-SQL on a 60M-Parameter Model
  7. 2026SQuaD-SQL: Efficient Text-to-SQL with Small Language Models via LLM-Guided Knowledge Distillation
  8. 2026Memo-SQL: Structured Decomposition and Experience-Driven Self-Correction for Training-Free NL2SQL