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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1–10 of 424 papers

Title	Date	Tasks	Status	Hype
CogniSQL-R1-Zero: Lightweight Reinforced Reasoning for Efficient SQL Generation	Jul 8, 2025	Reinforcement Learning (RL)TAG	—Unverified	0
XiYan-SQL: A Novel Multi-Generator Framework For Text-to-SQL	Jul 7, 2025	Text to SQLText-To-SQL	CodeCode Available	4
SWE-SQL: Illuminating LLM Pathways to Solve User SQL Issues in Real-World Applications	Jun 23, 2025	Text to SQLText-To-SQL	—Unverified	0
Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task	Jun 13, 2025	reinforcement-learningReinforcement Learning	CodeCode Available	1
HI-SQL: Optimizing Text-to-SQL Systems through Dynamic Hint Integration	Jun 11, 2025	Hint GenerationText to SQL	—Unverified	0
LLM-Driven Data Generation and a Novel Soft Metric for Evaluating Text-to-SQL in Aviation MRO	Jun 11, 2025	Text to SQLText-To-SQL	—Unverified	0
Bridging the Gap Between Open-Source and Proprietary LLMs in Table QA	Jun 11, 2025	Code GenerationLanguage Modeling	CodeCode Available	0
SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation	Jun 9, 2025	Natural Language QueriesText to SQL	CodeCode Available	1
SDE-SQL: Enhancing Text-to-SQL Generation in Large Language Models via Self-Driven Exploration with SQL Probes	Jun 8, 2025	Text to SQLText-To-SQL	—Unverified	0
SQLens: An End-to-End Framework for Error Detection and Correction in Text-to-SQL	Jun 4, 2025	Text to SQLText-To-SQL	—Unverified	0

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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