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Generative AI for Relational Database Management: A Comprehensive Review of Natural Language Interfaces for Text-to-SQL Conversion | ||
| Al-Noor Journal for Information Technology and Cybersecurity | ||
| Article 12, Volume 2, Issue 2, December 2025, Pages 85-94 PDF (418 K) | ||
| Document Type: Original Article | ||
| DOI: 10.69513/jncs.v2.i2.a12 | ||
| Authors | ||
| Yousif baderaldeen ahmed* ; Rayan Yousif Yaqoub | ||
| University of Mosul, College of Computer Science and Mathematics, Department of Computer Science, Iraq . | ||
| Abstract | ||
| A big problem with Natural Language Interfaces to Databases (NLIDBs) is that they can't turn natural language queries into SQL instructions. The primary causes are linguistic ambiguity, schema complexity, and the challenges non-experts face in articulating relational data objectives. This paper examines the progression of Text-to-SQL methodologies from rule-based and statistical frameworks to deep neural architectures and ultimately to Large Language Models. It compares them to the Spider and WikiSQL benchmarks. Evidence indicates that LLMs, particularly GPT-4-class models, enhance execution accuracy via contextual reasoning and in-context learning; nonetheless, challenges persist in multilingual generalization, complex query management, and robustness across various database schemas. The study also shows how quick engineering, schema linking, and retrieval-augmented methods may fill these gaps and lower the cost of making queries. These improvements show that data interaction models are moving toward ones that are easier to use, more conversational, and allow for more than one way to participate. This makes databases easier for non-technical individuals to use and helps people in various fields make decisions based on data. | ||
| Keywords | ||
| Generative AI; Relational Databases; Natural language Interface; SQL | ||
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