mlsanityData to 14 Oct 2026

Large Language Model Enhanced Text-to-SQL Generation: A Survey

arXiv 2410.06011 ↗First posted 8 Oct 2024cs.DBcited by 24

Not on the map. Why?

Abstract (arXiv)

Text-to-SQL translates natural language queries into Structured Query Language (SQL) commands, enabling users to interact with databases using natural language. Essentially, the text-to-SQL task is a text generation task, and its development is primarily dependent on changes in language models. Especially with the rapid development of Large Language Models (LLMs), the pattern of text-to-SQL has undergone significant changes. Existing survey work mainly focuses on rule-based and neural-based approaches, but it still lacks a survey of Text-to-SQL with LLMs. In this paper, we survey the large language model enhanced text-to-SQL generations, classifying them into prompt engineering, fine-tuning, pre-trained, and Agent groups according to training strategies. We also summarize datasets and evaluation metrics comprehensively. This survey could help people better understand the pattern, research status, and challenges of LLM-based text-to-SQL generations.

Key facts. Cited by 24 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Oct 2024.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 17; 2026: 7.

24 papers.

Sort:

By area:

  1. 2025Arctic-Text2SQL-R1: Simple Rewards, Strong Reasoning in Text-to-SQLcited by 38
  2. 2025LLM/Agent-as-Data-Analyst: A Surveycited by 16
  3. 2025Fundamentals of Building Autonomous LLM Agentscited by 8
  4. 2025Role-Conditioned Refusals: Evaluating Access Control Reasoning in Large Language Modelscited by 5
  5. 2025Rethinking Supply Chain Planning: A Generative Paradigmcited by 4
  6. 2025Rethinking Schema Linking: A Context-Aware Bidirectional Retrieval Approach for Text-to-SQLcited by 4
  7. 2025SchemaGraphSQL: Efficient Schema Linking with Pathfinding Graph Algorithms for Text-to-SQL on Large-Scale Databasescited by 3
  8. 2025Distill-C: Enhanced NL2SQL via Distilled Customization with LLMscited by 1
  9. 2025HES-SQL: Hybrid Reasoning for Efficient Text-to-SQL with Structural Skeleton Guidancecited by 1
  10. 2026Software-Defined Agentic Servingcited by 1
  11. 2026Both Ends Count! Just How Good are LLM Agents at "Text-to-Big SQL"?cited by 1
  12. 2026NL2SQLBench: A Modular Benchmarking Framework for LLM-Enabled NL2SQL Solutionscited by 1
12 more
  1. 2025SQLCritic: Correcting Text-to-SQL Generation via Clause-wise Criticcited by 0
  2. 2025Design and testing of an agent chatbot supporting decision making with public transport datacited by 0
  3. 2025SPOT: Bridging Natural Language and Geospatial Search for Investigative Journalistscited by 0
  4. 2025VizGen: Data Exploration and Visualization from Natural Language via a Multi-Agent AI Architecturecited by 0
  5. 2025FinAI Data Assistant: LLM-based Financial Database Query Processing with the OpenAI Function Calling APIcited by 0
  6. 2025BAPPA: Benchmarking Agents, Plans, and Pipelines for Automated Text-to-SQL Generationcited by 0
  7. 2025AskDB: An LLM Agent for Natural Language Interaction with Relational Databasescited by 0
  8. 2025SCARE: A Benchmark for SQL Correction and Question Answerability Classification for Reliable EHR Question Answeringcited by 0
  9. 2026RingSQL: Schema-Independent Synthetic Data Generation for Text-to-SQL Reinforcement Learningcited by 0
  10. 2026An Agentic System for Schema Aware NL2SQL Generationcited by 0
  11. 2026Natural Language Interfaces for Spatial and Temporal Databases: A Comprehensive Overview of Methods, Taxonomy, and Future Directionscited by 0
  12. 2026KGVoyager: Knowledge Graph Agnostic Question Answering via Agentic Navigationcited by 0