mlsanityData to 7 Oct 2026

Enhancing Text-to-SQL Capabilities of Large Language Models via Domain Database Knowledge Injection

arXiv 2409.15907 ↗First posted 24 Sep 2024cs.CL cs.AIcited by 2

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Abstract (arXiv)

Text-to-SQL is a subtask in semantic parsing that has seen rapid progress with the evolution of Large Language Models (LLMs). However, LLMs face challenges due to hallucination issues and a lack of domain-specific database knowledge(such as table schema and cell values). As a result, they can make errors in generating table names, columns, and matching values to the correct columns in SQL statements. This paper introduces a method of knowledge injection to enhance LLMs' ability to understand schema contents by incorporating prior knowledge. This approach improves their performance in Text-to-SQL tasks. Experimental results show that pre-training LLMs on domain-specific database knowledge and fine-tuning them on downstream Text-to-SQL tasks significantly improves the Execution Match (EX) and Exact Match (EM) metrics across various models. This effectively reduces errors in generating column names and matching values to the columns. Furthermore, the knowledge-injected models can be applied to many downstream Text-to-SQL tasks, demonstrating the generalizability of the approach presented in this paper.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 33 of its 35 references that are in the map. First posted on arXiv 24 Sep 2024.

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33 of 35 references.

References · 33

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21 more
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Citing papers per year: 2025: 2.

2 papers.

  1. 2025Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunitiescited by 4
  2. 2025Bridging the Semantic Gap: Contrastive Rewards for Multilingual Text-to-SQL with GRPOcited by 0