# From Natural Language to SQL: Review of LLM-based Text-to-SQL Systems

**Authors:** Ali Mohammadjafari, Anthony S. Maida, Raju Gottumukkala  
**arXiv:** [2410.01066](https://arxiv.org/abs/2410.01066)  
**Date:** 2024-10-01  
**Summary page:** https://www.summarizepaper.com/en/paper/2410.01066v1/

## Key points

- The paper discusses the evolution and impact of Large Language Models (LLMs) on translating natural language queries into structured SQL commands.
- It highlights the integration of knowledge graphs to enhance contextual accuracy and schema linking in text-to-SQL systems.
- Current techniques are categorized into in-context learning of corpus and fine-tuning, paving the way for advanced methods like zero-shot and few-shot learning.
- Key challenges faced by LLM-based text-to-SQL systems include computational efficiency, model robustness, and data privacy concerns.
- The review offers insights into potential areas for development and improvement to advance text-to-SQL technology.

## Summary

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The paper titled "From Natural Language to SQL: Review of LLM-based Text-to-SQL Systems" by Ali Mohammadjafari, Anthony S. Maida, and Raju Gottumukkala delves into the evolution and impact of Large Language Models (LLMs) on the process of translating natural language queries into structured SQL commands. The study provides a comprehensive overview of how LLM-based text-to-SQL systems have progressed from early rule-based models to advanced LLM approaches. It discusses the benchmarks, evaluation methods, and metrics used in assessing the performance of these systems. One key aspect highlighted in the paper is the integration of knowledge graphs to enhance contextual accuracy and schema linking within text-to-SQL systems. By incorporating knowledge graphs, these systems can better understand the relationships between different entities and improve query interpretation. The authors categorize current techniques into two main groups: in-context learning of corpus and fine-tuning. These approaches pave the way for more advanced methods such as zero-shot and few-shot learning, as well as data augmentation techniques. By leveraging these strategies, text-to-SQL systems can adapt to new scenarios and improve their overall performance. Furthermore, the paper addresses key challenges faced by LLM-based text-to-SQL systems, including computational efficiency, model robustness, and data privacy concerns. The authors provide insights into potential areas for development and improvement in these areas to ensure the continued advancement of text-to-SQL technology. In conclusion, this review offers a detailed analysis of how LLMs have revolutionized the field of text-to-SQL systems and outlines future directions for research and development in this domain.