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 376–400 of 424 papers

Title	Date	Tasks	Status
When Reasoning Beats Scale: A 1.5B Reasoning Model Outranks 13B LLMs as Discriminator	Apr 30, 2025	Text to SQLText-To-SQL	CodeCode Available
Valid Text-to-SQL Generation with Unification-based DeepStochLog	Mar 17, 2025	Language ModelingLanguage Modelling	CodeCode Available
Bertrand-DR: Improving Text-to-SQL using a Discriminative Re-ranker	Feb 3, 2020	Text to SQLText-To-SQL	CodeCode Available
AraSpider: Democratizing Arabic-to-SQL	Feb 12, 2024	Text to SQLText-To-SQL	CodeCode Available
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
Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing	May 15, 2019	DecoderGraph Neural Network	CodeCode Available
VTS-LLM: Domain-Adaptive LLM Agent for Enhancing Awareness in Vessel Traffic Services through Natural Language	May 2, 2025	ManagementNatural Language Queries	CodeCode Available
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
Integrating question answering and text-to-SQL in Portuguese	Feb 8, 2022	Question AnsweringText to SQL	CodeCode Available
Tracking Interaction States for Multi-Turn Text-to-SQL Semantic Parsing	Dec 9, 2020	Graph Neural NetworkSemantic Parsing	CodeCode Available
Robust Text-to-SQL Generation with Execution-Guided Decoding	Jul 9, 2018	Semantic ParsingText to SQL	CodeCode Available
I Need Help! Evaluating LLM's Ability to Ask for Users' Support: A Case Study on Text-to-SQL Generation	Jul 20, 2024	Text to SQLText-To-SQL	CodeCode Available
Improving Text-to-SQL Evaluation Methodology	Jun 23, 2018	SQL ParsingText to SQL	CodeCode Available
Improving Demonstration Diversity by Human-Free Fusing for Text-to-SQL	Feb 16, 2024	DiversityIn-Context Learning	CodeCode Available
ST-SQL: Semi-Supervised Self-Training for Text-to-SQL via Column Specificity Meta-Learning	Nov 16, 2021	Meta-LearningSpecificity	CodeCode Available
DAC: Decomposed Automation Correction for Text-to-SQL	Aug 16, 2024	Entity LinkingText to SQL	CodeCode Available
TrustSQL: Benchmarking Text-to-SQL Reliability with Penalty-Based Scoring	Mar 23, 2024	BenchmarkingText to SQL	CodeCode Available
Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsing	Jan 18, 2023	Domain GeneralizationInductive Bias	CodeCode Available
TUR2SQL: A Cross-Domain Turkish Dataset For Text-to-SQL	Oct 24, 2023	Text to SQLText-To-SQL	CodeCode Available
AnDB: Breaking Boundaries with an AI-Native Database for Universal Semantic Analysis	Feb 19, 2025	Semantic RetrievalText to SQL	CodeCode Available
SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Task	Oct 11, 2018	DecoderSemantic Parsing	CodeCode Available
Global Reasoning over Database Structures for Text-to-SQL Parsing	Aug 29, 2019	Graph Neural NetworkSemantic Parsing	CodeCode Available
TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generation	Apr 25, 2018	slot-fillingSlot Filling	CodeCode Available
SynTQA: Synergistic Table-based Question Answering via Mixture of Text-to-SQL and E2E TQA	Sep 25, 2024	Answer SelectionQuestion Answering	CodeCode Available
Federated Learning for Semantic Parsing: Task Formulation, Evaluation Setup, New Algorithms	May 26, 2023	Federated LearningSemantic Parsing	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