This is a page that curates AI-related papers published worldwide. All content here is summarized using Google Gemini and operated on a non-profit basis. Copyright for each paper belongs to the authors and their institutions; please make sure to credit the source when sharing.
To address the challenges of understanding complex database structures and accurately interpreting user intent in natural language query-based DBMS interactions using large-scale language models (LLMs), this paper proposes a novel Structure-Guided Text-to-SQL framework (SGU-SQL) that integrates structure-based prompting. SGU-SQL enables more accurate LLM-based SQL generation by establishing structure-aware connections between user queries and database schemas and decomposing the complex generation task using syntax-based prompting. Through extensive experiments on two benchmark datasets, we show that SGU-SQL consistently outperforms state-of-the-art text-to-SQL models.
Takeaways, Limitations
•
Takeaways:
◦
A novel approach to LLM-based SQL generation using syntax-based prompting
◦
Improve accuracy through structural linkage between user queries and database schema.
◦
Practicality proven with superior performance compared to cutting-edge models
•
Limitations:
◦
Further research is needed on the generalizability of the proposed framework.
◦
Robustness evaluation for various types of database schemas and queries is required.
◦
Need to examine the generalizability of performance evaluation results for specific datasets
