Text-To-SQL

Text-to-SQL is a task in natural language processing (NLP) where the goal is to automatically generate SQL queries from natural language text. The task involves converting the text input into a structured representation and then using this representation to generate a semantically correct SQL query that can be executed on a database.

( Image credit: SyntaxSQLNet )

Papers

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Showing 176–200 of 424 papers

Title	Date	Tasks	Status	Score
ColloQL: Robust Text-to-SQL Over Search Queries	Nov 1, 2020	Data AugmentationText to SQL	CodeCode Available	5
Exploring Underexplored Limitations of Cross-Domain Text-to-SQL Generalization	Sep 11, 2021	Text to SQLText-To-SQL	CodeCode Available	5
MAGNIFICo: Evaluating the In-Context Learning Ability of Large Language Models to Generalize to Novel Interpretations	Oct 18, 2023	In-Context LearningSemantic Parsing	CodeCode Available	5
Confidence Estimation for Error Detection in Text-to-SQL Systems	Jan 16, 2025	DecoderIn-Context Learning	CodeCode Available	5
Benchmarking and Improving Text-to-SQL Generation under Ambiguity	Oct 20, 2023	BenchmarkingDiversity	CodeCode Available	5
MURRE: Multi-Hop Table Retrieval with Removal for Open-Domain Text-to-SQL	Feb 16, 2024	Open-Domain Question AnsweringQuestion Answering	CodeCode Available	5
ColloQL: Robust Cross-Domain Text-to-SQL Over Search Queries	Oct 19, 2020	Data AugmentationText to SQL	CodeCode Available	5
Explainable Multi-Modal Data Exploration in Natural Language via LLM Agent	Dec 24, 2024	Text to SQLText-To-SQL	CodeCode Available	5
AnDB: Breaking Boundaries with an AI-Native Database for Universal Semantic Analysis	Feb 19, 2025	Semantic RetrievalText to SQL	CodeCode Available	5
Text-to-SQL Generation for Question Answering on Electronic Medical Records	Jul 28, 2019	Information RetrievalQuestion Answering	CodeCode Available	5
Integrating question answering and text-to-SQL in Portuguese	Feb 8, 2022	Question AnsweringText to SQL	CodeCode Available	5
LLM-SQL-Solver: Can LLMs Determine SQL Equivalence?	Dec 16, 2023	Question AnsweringText to SQL	CodeCode Available	5
Evaluating the Data Model Robustness of Text-to-SQL Systems Based on Real User Queries	Feb 13, 2024	Language ModellingText to SQL	CodeCode Available	5
A Tale of Two Linkings: Dynamically Gating between Schema Linking and Structural Linking for Text-to-SQL Parsing	Sep 30, 2020	Graph Neural NetworkSemantic Parsing	CodeCode Available	5
JOLT-SQL: Joint Loss Tuning of Text-to-SQL with Confusion-aware Noisy Schema Sampling	May 20, 2025	Text to SQLText-To-SQL	CodeCode Available	5
LogicCat: A Chain-of-Thought Text-to-SQL Benchmark for Multi-Domain Reasoning Challenges	May 24, 2025	BenchmarkingMathematical Reasoning	CodeCode Available	5
Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types	Dec 21, 2024	MMSQL performanceNavigate	CodeCode Available	5
ESM+: Modern Insights into Perspective on Text-to-SQL Evaluation in the Age of Large Language Models	Jul 10, 2024	set matchingText to SQL	CodeCode Available	5
Non-Programmers Can Label Programs Indirectly via Active Examples: A Case Study with Text-to-SQL	May 25, 2022	Bayesian InferenceText to SQL	CodeCode Available	5
Leveraging Prior Experience: An Expandable Auxiliary Knowledge Base for Text-to-SQL	Nov 20, 2024	Continual LearningIn-Context Learning	CodeCode Available	5
Error Detection for Text-to-SQL Semantic Parsing	May 23, 2023	Language ModelingLanguage Modelling	CodeCode Available	5
BiomedSQL: Text-to-SQL for Scientific Reasoning on Biomedical Knowledge Bases	May 23, 2025	Causal Inferencescientific discovery	CodeCode Available	5
Large Language Models are Good Multi-lingual Learners : When LLMs Meet Cross-lingual Prompts	Sep 17, 2024	Text to SQLText-To-SQL	CodeCode Available	5
Enhancing Open-Domain Table Question Answering via Syntax- and Structure-aware Dense Retrieval	Sep 19, 2023	Question AnsweringRetrieval	CodeCode Available	5
Learn from Yesterday: A Semi-Supervised Continual Learning Method for Supervision-Limited Text-to-SQL Task Streams	Nov 21, 2022	Continual LearningText to SQL	CodeCode Available	5

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All datasets BIRD (BIg Bench for LaRge-scale Database Grounded Text-to-SQL Evaluation)spider Spider 2.0 SParC KaggleDBQA SEDE SQL-Eval Text-To-SQL

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Human Performance	Execution Accurarcy (Human)	92.96	—	Unverified
2	XiYan-SQL	Execution Accuracy % (Test)	75.63	—	Unverified
3	DSAIR + GPT-4o	Execution Accuracy % (Test)	74.12	—	Unverified
4	CHASE-SQL + Gemini	Execution Accuracy % (Test)	74.06	—	Unverified
5	ExSL + granite-34b-code	Execution Accuracy % (Test)	73.17	—	Unverified
6	OpenSearch-SQL+ v2 + GPT-4o	Execution Accuracy % (Test)	72.28	—	Unverified
7	Distillery + GPT-4o	Execution Accuracy % (Test)	71.83	—	Unverified
8	Insights AI	Execution Accuracy % (Test)	70.26	—	Unverified
9	PURPLE + RED + GPT-4o	Execution Accuracy % (Test)	70.21	—	Unverified
10	MCTS-SQL	Execution Accuracy % (Test)	69.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	XiYan-SQL	Execution Accuracy (Test)	89.65	—	Unverified
2	PET-SQL	Execution Accuracy (Test)	87.6	—	Unverified
3	datagpt-sql-7B + InvalidSQL-Feedback	Execution Accuracy (Dev)	87.2	—	Unverified
4	DAIL-SQL + GPT-4 + Self-Consistency	Execution Accuracy (Test)	86.6	—	Unverified
5	DIN-SQL + GPT-4	Execution Accuracy (Test)	85.3	—	Unverified
6	datagpt-sql-7B	Execution Accuracy (Dev)	84.8	—	Unverified
7	MSc-SQL	Execution Accuracy (Test)	84.7	—	Unverified
8	MARLO + Claude 2.1	Execution Accuracy (Test)	84	—	Unverified
9	C3 + ChatGPT + Zero-Shot	Execution Accuracy (Test)	82.3	—	Unverified
10	code-davinci-002 175B (LEVER)	Execution Accuracy (Dev)	81.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Spider-Agent + o1-preview	Success Rate	17.03	—	Unverified
2	Spider-Agent + GPT-4o	Success Rate	10.13	—	Unverified
3	Spider-Agent + Claude-3.5-Sonnect	Success Rate	9.02	—	Unverified
4	Spider-Agent + GPT-4	Success Rate	8.86	—	Unverified
5	Spider-Agent + Qwen2.5-72B	Success Rate	6.17	—	Unverified
6	Spider-Agent + DeepSeek-V2.5	Success Rate	5.22	—	Unverified
7	Spider-Agent + Gemini-Pro-1.5	Success Rate	2.53	—	Unverified
8	Spider-Agent + Llama-3.1-405B	Success Rate	2.21	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	RASAT+PICARD	interaction match accuracy	45.2	—	Unverified
2	RAT-SQL-TC + GAP	interaction match accuracy	43.2	—	Unverified
3	HIE-SQL + GraPPa	interaction match accuracy	42.9	—	Unverified
4	RAT-SQL + SCoRe	interaction match accuracy	38.1	—	Unverified
5	EditSQL + BERT	interaction match accuracy	25.3	—	Unverified
6	GAZP + BERT	interaction match accuracy	23.5	—	Unverified
7	SyntaxSQL-con	interaction match accuracy	5.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	RAT-SQL	Exact Match (EM)	26.77	—	Unverified
2	Edit-SQL	Exact Match (EM)	11.73	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	T5-Large	PCM-F1 (dev)	48.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	XiYan-SQL	Execution Accuracy	69.86	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Orange-mini	0-shot MRR	74.17	—	Unverified