DCG-SQL: Enhancing In-Context Learning for Text-to-SQL with Deep Contextual Schema Link Graph

• URL: http://arxiv.org/abs/2505.19956v2
• Date: Tue, 22 Jul 2025 08:42:57 GMT
• Title: DCG-SQL: Enhancing In-Context Learning for Text-to-SQL with Deep Contextual Schema Link Graph
• Authors: Jihyung Lee, Jin-Seop Lee, Jaehoon Lee, YunSeok Choi, Jee-Hyong Lee,
• Abstract summary: We propose a novel approach for effectively retrieving demonstrations and generating queries.<n>We construct a Deep Contextual Link Graph, which contains key information and relationship between a question and its database schema items.
• Score: 14.637276700334018
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text-to-SQL, which translates a natural language question into an SQL query, has advanced with in-context learning of Large Language Models (LLMs). However, existing methods show little improvement in performance compared to randomly chosen demonstrations, and significant performance drops when smaller LLMs (e.g., Llama 3.1-8B) are used. This indicates that these methods heavily rely on the intrinsic capabilities of hyper-scaled LLMs, rather than effectively retrieving useful demonstrations. In this paper, we propose a novel approach for effectively retrieving demonstrations and generating SQL queries. We construct a Deep Contextual Schema Link Graph, which contains key information and semantic relationship between a question and its database schema items. This graph-based structure enables effective representation of Text-to-SQL samples and retrieval of useful demonstrations for in-context learning. Experimental results on the Spider benchmark demonstrate the effectiveness of our approach, showing consistent improvements in SQL generation performance and efficiency across both hyper-scaled LLMs and small LLMs. The code is available at https://github.com/jjklle/DCG-SQL}{https://github.com/jjklle/DCG-SQL.

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