# Enhancing LLM Fine-tuning for Text-to-SQLs by SQL Quality Measurement

**Authors:** Shouvon Sarker, Xishuang Dong, Xiangfang Li, Lijun Qian  
**arXiv:** [2410.01869](https://arxiv.org/abs/2410.01869)  
**Date:** 2024-10-02  
**Summary page:** https://www.summarizepaper.com/en/paper/2410.01869v1/

## Key points

- Authors propose a novel approach focusing on SQL Quality Measurement to enhance LLM-based Text-to-SQL systems
- Method introduces mechanism for evaluating quality of generated SQL queries against predefined criteria and actual database responses
- Model continuously learns and refines outputs based on syntactic correctness and semantic accuracy through feedback loop
- Comprehensive experiments conducted on BIRD benchmark show competitive performance in Execution Accuracy (EX) and Valid Efficiency Score (VES) metrics compared to state-of-the-art models like GPT4 and T5
- Research emphasizes the importance of incorporating SQL quality evaluation mechanisms in LLM-based Text-to-SQL systems to improve overall performance and usability for non-expert users

## Summary

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In their paper titled "Enhancing LLM Fine-tuning for Text-to-SQLs by SQL Quality Measurement," authors Shouvon Sarker, Xishuang Dong, Xiangfang Li, and Lijun Qian delve into the realm of Text-to-SQL systems that allow non-expert users to effortlessly retrieve information from relational databases using natural language queries. The authors propose a novel approach that focuses solely on SQL Quality Measurement to enhance the performance of LLM-based Text-to-SQL systems. This method introduces a mechanism for evaluating the quality of generated SQL queries against predefined criteria and actual database responses. By establishing this feedback loop, the model can continuously learn and refine its outputs based on both syntactic correctness and semantic accuracy. To validate their proposed approach, the authors conduct comprehensive experiments on the BIRD benchmark, evaluating Execution Accuracy (EX) and Valid Efficiency Score (VES) across various levels of Text-to-SQL difficulty. The experimental results demonstrate competitive performance in both EX and VES metrics when compared to state-of-the-art models like GPT4 and T5. Overall, this research highlights the significance of incorporating SQL quality evaluation mechanisms in LLM-based Text-to-SQL systems to improve their overall performance and usability for non-expert users seeking information retrieval from relational databases through natural language queries.