SOTAVerified

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 101–150 of 424 papers

TitleStatusHype
Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic ParsingCode1
Did You Ask a Good Question? A Cross-Domain Question Intention Classification Benchmark for Text-to-SQLCode1
CoE-SQL: In-Context Learning for Multi-Turn Text-to-SQL with Chain-of-EditionsCode1
SPARQLing Database Queries from Intermediate Question DecompositionsCode1
SpCQL: A Semantic Parsing Dataset for Converting Natural Language into CypherCode1
Hybrid Ranking Network for Text-to-SQLCode1
Semantic Evaluation for Text-to-SQL with Distilled Test SuitesCode1
SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence GenerationCode1
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement LearningCode1
MAG-SQL: Multi-Agent Generative Approach with Soft Schema Linking and Iterative Sub-SQL Refinement for Text-to-SQLCode1
GraPPa: Grammar-Augmented Pre-Training for Table Semantic ParsingCode1
Analyzing the Effectiveness of Large Language Models on Text-to-SQL SynthesisCode1
A Study of In-Context-Learning-Based Text-to-SQL ErrorsCode1
Dubo-SQL: Diverse Retrieval-Augmented Generation and Fine Tuning for Text-to-SQLCode1
Improving Generalization in Language Model-Based Text-to-SQL Semantic Parsing: Two Simple Semantic Boundary-Based TechniquesCode1
Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLsCode1
Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic ParsingCode1
Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?Code1
TaBERT: Pretraining for Joint Understanding of Textual and Tabular DataCode1
EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health RecordsCode1
ValueNet: A Natural Language-to-SQL System that Learns from Database InformationCode1
Robust Text-to-SQL Generation with Execution-Guided DecodingCode0
Encoding Database Schemas with Relation-Aware Self-Attention for Text-to-SQL ParsersCode0
Representing Schema Structure with Graph Neural Networks for Text-to-SQL ParsingCode0
Editing-Based SQL Query Generation for Cross-Domain Context-Dependent QuestionsCode0
Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL ParsingCode0
ProbGate at EHRSQL 2024: Enhancing SQL Query Generation Accuracy through Probabilistic Threshold Filtering and Error HandlingCode0
Byte-Pair Encoding for Text-to-SQL GenerationCode0
Pay More Attention to History: A Context Modelling Strategy for Conversational Text-to-SQLCode0
OpenGrok: Enhancing SNS Data Processing with Distilled Knowledge and Mask-like MechanismsCode0
PTD-SQL: Partitioning and Targeted Drilling with LLMs in Text-to-SQLCode0
Disambiguate First Parse Later: Generating Interpretations for Ambiguity Resolution in Semantic ParsingCode0
Bridging the Gap Between Open-Source and Proprietary LLMs in Table QACode0
NL-EDIT: Correcting semantic parse errors through natural language interactionCode0
PG-GSQL: Pointer-Generator Network with Guide Decoding for Cross-Domain Context-Dependent Text-to-SQL GenerationCode0
MURRE: Multi-Hop Table Retrieval with Removal for Open-Domain Text-to-SQLCode0
AirConcierge: Generating Task-Oriented Dialogue via Efficient Large-Scale Knowledge RetrievalCode0
MultiSpider: Towards Benchmarking Multilingual Text-to-SQL Semantic ParsingCode0
BiomedSQL: Text-to-SQL for Scientific Reasoning on Biomedical Knowledge BasesCode0
Bertrand-DR: Improving Text-to-SQL using a Discriminative Re-rankerCode0
LogicCat: A Chain-of-Thought Text-to-SQL Benchmark for Multi-Domain Reasoning ChallengesCode0
LLM-SQL-Solver: Can LLMs Determine SQL Equivalence?Code0
Benchmarking and Improving Text-to-SQL Generation under AmbiguityCode0
DataGpt-SQL-7B: An Open-Source Language Model for Text-to-SQLCode0
AraSpider: Democratizing Arabic-to-SQLCode0
Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL ParsingCode0
DAC: Decomposed Automation Correction for Text-to-SQLCode0
CSS: A Large-scale Cross-schema Chinese Text-to-SQL Medical DatasetCode0
Leveraging Prior Experience: An Expandable Auxiliary Knowledge Base for Text-to-SQLCode0
CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to DatabasesCode0
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All datasetsBIRD (BIg Bench for LaRge-scale Database Grounded Text-to-SQL Evaluation)spiderSpider 2.0SParCKaggleDBQASEDESQL-EvalText-To-SQL

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Human PerformanceExecution Accurarcy (Human)92.96—Unverified
2XiYan-SQLExecution Accuracy % (Test)75.63—Unverified
3DSAIR + GPT-4oExecution Accuracy % (Test)74.12—Unverified
4CHASE-SQL + GeminiExecution Accuracy % (Test)74.06—Unverified
5ExSL + granite-34b-codeExecution Accuracy % (Test)73.17—Unverified
6OpenSearch-SQL+ v2 + GPT-4oExecution Accuracy % (Test)72.28—Unverified
7Distillery + GPT-4oExecution Accuracy % (Test)71.83—Unverified
8Insights AIExecution Accuracy % (Test)70.26—Unverified
9PURPLE + RED + GPT-4oExecution Accuracy % (Test)70.21—Unverified
10MCTS-SQLExecution Accuracy % (Test)69.4—Unverified
#ModelMetricClaimedVerifiedStatus
1XiYan-SQLExecution Accuracy (Test)89.65—Unverified
2PET-SQLExecution Accuracy (Test)87.6—Unverified
3datagpt-sql-7B + InvalidSQL-FeedbackExecution Accuracy (Dev)87.2—Unverified
4DAIL-SQL + GPT-4 + Self-ConsistencyExecution Accuracy (Test)86.6—Unverified
5DIN-SQL + GPT-4Execution Accuracy (Test)85.3—Unverified
6datagpt-sql-7BExecution Accuracy (Dev)84.8—Unverified
7MSc-SQLExecution Accuracy (Test)84.7—Unverified
8MARLO + Claude 2.1Execution Accuracy (Test)84—Unverified
9C3 + ChatGPT + Zero-ShotExecution Accuracy (Test)82.3—Unverified
10code-davinci-002 175B (LEVER)Execution Accuracy (Dev)81.9—Unverified
#ModelMetricClaimedVerifiedStatus
1Spider-Agent + o1-previewSuccess Rate17.03—Unverified
2Spider-Agent + GPT-4oSuccess Rate10.13—Unverified
3Spider-Agent + Claude-3.5-SonnectSuccess Rate9.02—Unverified
4Spider-Agent + GPT-4Success Rate8.86—Unverified
5Spider-Agent + Qwen2.5-72BSuccess Rate6.17—Unverified
6Spider-Agent + DeepSeek-V2.5Success Rate5.22—Unverified
7Spider-Agent + Gemini-Pro-1.5Success Rate2.53—Unverified
8Spider-Agent + Llama-3.1-405BSuccess Rate2.21—Unverified
#ModelMetricClaimedVerifiedStatus
1RASAT+PICARDinteraction match accuracy45.2—Unverified
2RAT-SQL-TC + GAPinteraction match accuracy43.2—Unverified
3HIE-SQL + GraPPainteraction match accuracy42.9—Unverified
4RAT-SQL + SCoReinteraction match accuracy38.1—Unverified
5EditSQL + BERTinteraction match accuracy25.3—Unverified
6GAZP + BERTinteraction match accuracy23.5—Unverified
7SyntaxSQL-coninteraction match accuracy5.2—Unverified
#ModelMetricClaimedVerifiedStatus
1RAT-SQLExact Match (EM)26.77—Unverified
2Edit-SQLExact Match (EM)11.73—Unverified
#ModelMetricClaimedVerifiedStatus
1T5-LargePCM-F1 (dev)48.2—Unverified
#ModelMetricClaimedVerifiedStatus
1XiYan-SQLExecution Accuracy69.86—Unverified
#ModelMetricClaimedVerifiedStatus
1Orange-mini0-shot MRR74.17—Unverified