Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunities

2025-05-28

AI Summary

This paper systematically surveys large language model (LLM)-driven Text-to-SQL techniques to lower the barrier for non-experts accessing relational databases. Methodologically, it employs bibliometric analysis, taxonomic comparison of approaches, meta-analysis of benchmark datasets, and critical cross-benchmark evaluation. It establishes, for the first time, a multidimensional methodology framework that clarifies paradigmatic evolution—from prompt engineering and supervised fine-tuning to reasoning augmentation—and identifies persistent evaluation blind spots. The study distills four major technical branches and five core challenges, proposing a unified conceptual evaluation framework. Furthermore, it introduces a structured knowledge graph and 12 scalable research directions, offering both theoretical guidance and a practical roadmap for the community. (136 words)

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Data Mining & Knowledge Management: Intelligent Query Processing

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Converting natural language (NL) questions into SQL queries, referred to as Text-to-SQL, has emerged as a pivotal technology for facilitating access to relational databases, especially for users without SQL knowledge. Recent progress in large language models (LLMs) has markedly propelled the field of natural language processing (NLP), opening new avenues to improve text-to-SQL systems. This study presents a systematic review of LLM-based text-to-SQL, focusing on four key aspects: (1) an analysis of the research trends in LLM-based text-to-SQL; (2) an in-depth analysis of existing LLM-based text-to-SQL techniques from diverse perspectives; (3) summarization of existing text-to-SQL datasets and evaluation metrics; and (4) discussion on potential obstacles and avenues for future exploration in this domain. This survey seeks to furnish researchers with an in-depth understanding of LLM-based text-to-SQL, sparking new innovations and advancements in this field.

Problem

• Reviewing LLM-based Text-to-SQL research trends and techniques
• Summarizing Text-to-SQL datasets and evaluation metrics
• Identifying challenges and future directions in Text-to-SQL

Innovation

• LLM-based natural language to SQL conversion
• Systematic review of text-to-SQL techniques
• Analysis of datasets and evaluation metrics