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 401–424 of 424 papers

Title	Date	Tasks	Status
Uncovering and Categorizing Social Biases in Text-to-SQL	May 25, 2023	Text to SQLText-To-SQL	CodeCode Available
Exploring Underexplored Limitations of Cross-Domain Text-to-SQL Generalization	Sep 11, 2021	Text to SQLText-To-SQL	CodeCode Available
Selective Demonstrations for Cross-domain Text-to-SQL	Oct 10, 2023	In-Context LearningText to SQL	CodeCode Available
SelECT-SQL: Self-correcting ensemble Chain-of-Thought for Text-to-SQL	Sep 16, 2024	In-Context LearningText to SQL	CodeCode Available
T5-SR: A Unified Seq-to-Seq Decoding Strategy for Semantic Parsing	Jun 14, 2023	Natural Language QueriesReranking	CodeCode Available
Semantic Decomposition of Question and SQL for Text-to-SQL Parsing	Oct 20, 2023	RetrievalSemantic Parsing	CodeCode Available
Explainable Multi-Modal Data Exploration in Natural Language via LLM Agent	Dec 24, 2024	Text to SQLText-To-SQL	CodeCode Available
Evaluating the Data Model Robustness of Text-to-SQL Systems Based on Real User Queries	Feb 13, 2024	Language ModellingText to SQL	CodeCode Available
CSS: A Large-scale Cross-schema Chinese Text-to-SQL Medical Dataset	May 25, 2023	BenchmarkingText to SQL	CodeCode Available
CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases	Sep 11, 2019	Dialogue State TrackingResponse Generation	CodeCode Available
Semi-Automatic Construction of Text-to-SQL Data for Domain Transfer	Aug 1, 2021	Text to SQLText-To-SQL	CodeCode Available
Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types	Dec 21, 2024	MMSQL performanceNavigate	CodeCode Available
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
Correcting Semantic Parses with Natural Language through Dynamic Schema Encoding	May 31, 2023	Semantic ParsingText to SQL	CodeCode Available
Benchmarking and Improving Text-to-SQL Generation under Ambiguity	Oct 20, 2023	BenchmarkingDiversity	CodeCode Available
ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parser	Apr 10, 2021	DecoderSemantic Parsing	CodeCode Available
Content Enhanced BERT-based Text-to-SQL Generation	Oct 16, 2019	Code GenerationSemantic Parsing	CodeCode Available
Sigma: A dataset for text-to-code semantic parsing with statistical analysis	Apr 5, 2025	Question AnsweringSemantic Parsing	CodeCode Available
Teaching Large Language Models to Self-Debug	Apr 11, 2023	Code GenerationLanguage Modeling	CodeCode Available
SParC: Cross-Domain Semantic Parsing in Context	Jun 5, 2019	DiversitySemantic Parsing	CodeCode Available
Confidence Estimation for Error Detection in Text-to-SQL Systems	Jan 16, 2025	DecoderIn-Context Learning	CodeCode Available
Error Detection for Text-to-SQL Semantic Parsing	May 23, 2023	Language ModelingLanguage Modelling	CodeCode Available
Enhancing Open-Domain Table Question Answering via Syntax- and Structure-aware Dense Retrieval	Sep 19, 2023	Question AnsweringRetrieval	CodeCode Available
Automated Self-Refinement and Self-Correction for LLM-based Product Attribute Value Extraction	Jan 2, 2025	AttributeAttribute Value Extraction	CodeCode Available

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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