# A Survey on Employing Large Language Models for Text-to-SQL Tasks

arXiv: https://arxiv.org/abs/2407.15186 · HTML: https://mlsanity.com/paper/2407.15186/

## Key facts

- Cited by 67 papers in the map (data through 14 Oct 2026).
- Builds on 108 of its 162 references that are in the map.
- First posted on arXiv 21 Jul 2024.

## Builds on (most cited first, 30 of 108)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2204.05862/) (2022)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence](https://mlsanity.com/paper/2401.14196/) (2024)
- [PaLM 2 Technical Report](https://mlsanity.com/paper/2305.10403/) (2023)
- [Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task](https://mlsanity.com/paper/1809.08887/) (2018)
- [Teaching Large Language Models to Self-Debug](https://mlsanity.com/paper/2304.05128/) (2023)
- [Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning](https://mlsanity.com/paper/2205.05638/) (2022)
- [CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis](https://mlsanity.com/paper/2203.13474/) (2022)
- [StarCoder: may the source be with you!](https://mlsanity.com/paper/2305.06161/) (2023)

## Built on by (most cited first, 30 of 67)

- [A Survey of Text-to-SQL in the Era of LLMs: Where are we, and where are we going?](https://mlsanity.com/paper/2408.05109/) (2024)
- [In-Context Learning Is Provably Bayesian Inference: A Generalization Theory for Meta-Learning](https://mlsanity.com/paper/2510.10981/) (2025)
- [BIRD-INTERACT: Re-imagining Text-to-SQL Evaluation for Large Language Models via Lens of Dynamic Interactions](https://mlsanity.com/paper/2510.05318/) (2025)
- [AutoLink: Autonomous Schema Exploration and Expansion for Scalable Schema Linking in Text-to-SQL at Scale](https://mlsanity.com/paper/2511.17190/) (2025)
- [LLM/Agent-as-Data-Analyst: A Survey](https://mlsanity.com/paper/2509.23988/) (2025)
- [CSC-SQL: Corrective Self-Consistency in Text-to-SQL via Reinforcement Learning](https://mlsanity.com/paper/2505.13271/) (2025)
- [FLEX: Expert-level False-Less EXecution Metric for Reliable Text-to-SQL Benchmark](https://mlsanity.com/paper/2409.19014/) (2024)
- [SQLForge: Synthesizing Reliable and Diverse Data to Enhance Text-to-SQL Reasoning in LLMs](https://mlsanity.com/paper/2505.13725/) (2025)
- [A Survey of Large Language Model-Based Generative AI for Text-to-SQL: Benchmarks, Applications, Use Cases, and Challenges](https://mlsanity.com/paper/2412.05208/) (2024)
- [Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework](https://mlsanity.com/paper/2412.12612/) (2024)
- [SLM-SQL: An Exploration of Small Language Models for Text-to-SQL](https://mlsanity.com/paper/2507.22478/) (2025)
- [BASE-SQL: A powerful open source Text-To-SQL baseline approach](https://mlsanity.com/paper/2502.10739/) (2025)
- [APEX-SQL: Talking to the data via Agentic Exploration for Text-to-SQL](https://mlsanity.com/paper/2602.16720/) (2026)
- [Mixture-of-Minds: Multi-Agent Reinforcement Learning for Table Understanding](https://mlsanity.com/paper/2510.20176/) (2025)
- [DAC: Decomposed Automation Correction for Text-to-SQL](https://mlsanity.com/paper/2408.08779/) (2024)
- [SQL-of-Thought: Multi-agentic Text-to-SQL with Guided Error Correction](https://mlsanity.com/paper/2509.00581/) (2025)
- [Towards Action Hijacking of Large Language Model-based Agent](https://mlsanity.com/paper/2412.10807/) (2024)
- [MCTS-SQL: Light-Weight LLMs can Master the Text-to-SQL through Monte Carlo Tree Search](https://mlsanity.com/paper/2501.16607/) (2025)
- [JOLT-SQL: Joint Loss Tuning of Text-to-SQL with Confusion-aware Noisy Schema Sampling](https://mlsanity.com/paper/2505.14305/) (2025)
- [SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation](https://mlsanity.com/paper/2506.07423/) (2025)
- [SpotIt: Evaluating Text-to-SQL Evaluation with Formal Verification](https://mlsanity.com/paper/2510.26840/) (2025)
- [RulePilot: An LLM-Powered Agent for Security Rule Generation](https://mlsanity.com/paper/2511.12224/) (2025)
- [LLM and Agent-Driven Data Analysis: A Systematic Approach for Enterprise Applications and System-level Deployment](https://mlsanity.com/paper/2511.17676/) (2025)
- [MaskSQL: Safeguarding Privacy for LLM-Based Text-to-SQL via Abstraction](https://mlsanity.com/paper/2509.23459/) (2025)
- [Chatty-KG: A Multi-Agent AI System for On-Demand Conversational Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2511.20940/) (2025)
- [Semantic Layers for Reliable LLM-Powered Data Analytics: A Paired Benchmark of Accuracy and Hallucination Across Three Frontier Models](https://mlsanity.com/paper/2604.25149/) (2026)
- [Balancing Content Size in RAG-Text2SQL System](https://mlsanity.com/paper/2502.15723/) (2025)
- [RetrySQL: text-to-SQL training with retry data for self-correcting query generation](https://mlsanity.com/paper/2507.02529/) (2025)
- [STARQA: A Question Answering Dataset for Complex Analytical Reasoning over Structured Databases](https://mlsanity.com/paper/2509.19508/) (2025)
- [GeoSQL-Eval: First Evaluation of LLMs on PostGIS-Based NL2GeoSQL Queries](https://mlsanity.com/paper/2509.25264/) (2025)

## Abstract (arXiv)

With the development of the Large Language Models (LLMs), a large range of LLM-based Text-to-SQL(Text2SQL) methods have emerged. This survey provides a comprehensive review of LLM-based Text2SQL studies. We first enumerate classic benchmarks and evaluation metrics. For the two mainstream methods, prompt engineering and finetuning, we introduce a comprehensive taxonomy and offer practical insights into each subcategory. We present an overall analysis of the above methods and various models evaluated on well-known datasets and extract some characteristics. Finally, we discuss the challenges and future directions in this field.