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 351–400 of 424 papers

TitleStatusHype
Pretrained Language Models Are All You Need For Text-to-SQL Schema Linking—0
Pay More Attention to History: A Context Modelling Strategy for Conversational Text-to-SQLCode0
Hierarchical Neural Data Synthesis for Semantic Parsing—0
UniSAr: A Unified Structure-Aware Autoregressive Language Model for Text-to-SQL—0
HIE-SQL: History Information Enhanced Network for Context-Dependent Text-to-SQL Semantic Parsing—0
DrugEHRQA: A Question Answering Dataset on Structured and Unstructured Electronic Health Records For Medicine Related Queries—0
S^2SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers—0
ST-SQL: Semi-Supervised Self-Training for Text-to-SQL via Column Specificity Meta-LearningCode0
Towards Robustness of Text-to-SQL Models Against Natural and Realistic Adversarial Table Perturbation—0
Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsing—0
Evaluating the Text-to-SQL Capabilities of Large Language Models—0
Speech-to-SQL Parsing: Error Correction with Multi-modal Representations—0
Measuring and Improving Compositional Generalization in Text-to-SQL via Component Alignment—0
Awakening Latent Grounding from Pretrained Language Models for Semantic Parsing—0
SeaD: End-to-end Text-to-SQL Generation with Schema-aware Denoising—0
Prefix-to-SQL: Text-to-SQL Generation from Incomplete User Questions—0
Exploring Underexplored Limitations of Cross-Domain Text-to-SQL GeneralizationCode0
Chase: A Large-Scale and Pragmatic Chinese Dataset for Cross-Database Context-Dependent Text-to-SQL—0
Semi-Automatic Construction of Text-to-SQL Data for Domain TransferCode0
KaggleDBQA: Realistic Evaluation of Text-to-SQL ParsersCode0
End-to-End Cross-Domain Text-to-SQL Semantic Parsing with Auxiliary Task—0
Turing: an Accurate and Interpretable Multi-Hypothesis Cross-Domain Natural Language Database Interface—0
Decoupled Dialogue Modeling and Semantic Parsing for Multi-Turn Text-to-SQL—0
ShadowGNN: Graph Projection Neural Network for Text-to-SQL ParserCode0
NL-EDIT: Correcting semantic parse errors through natural language interactionCode0
Self-supervised Text-to-SQL Learning with Header Alignment Training—0
Improving Text-to-SQL with Schema Dependency Learning—0
Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsing—0
GP: Context-free Grammar Pre-training for Text-to-SQL Parsers—0
SCoRe: Pre-Training for Context Representation in Conversational Semantic Parsing—0
MT-Teql: Evaluating and Augmenting Consistency of Text-to-SQL Models with Metamorphic Testing—0
Mention Extraction and Linking for SQL Query Generation—0
Tracking Interaction States for Multi-Turn Text-to-SQL Semantic ParsingCode0
SQL Generation via Machine Reading ComprehensionCode0
A Review of Cross-Domain Text-to-SQL Models—0
PG-GSQL: Pointer-Generator Network with Guide Decoding for Cross-Domain Context-Dependent Text-to-SQL GenerationCode0
AirConcierge: Generating Task-Oriented Dialogue via Efficient Large-Scale Knowledge RetrievalCode0
"What Do You Mean by That?" A Parser-Independent Interactive Approach for Enhancing Text-to-SQL—0
Re-examining the Role of Schema Linking in Text-to-SQL—0
``What Do You Mean by That?'' A Parser-Independent Interactive Approach for Enhancing Text-to-SQL—0
DuSQL: A Large-Scale and Pragmatic Chinese Text-to-SQL Dataset—0
ColloQL: Robust Text-to-SQL Over Search QueriesCode0
Service-oriented Text-to-SQL Parsing—0
Structure-Grounded Pretraining for Text-to-SQL—0
ColloQL: Robust Cross-Domain Text-to-SQL Over Search QueriesCode0
A Tale of Two Linkings: Dynamically Gating between Schema Linking and Structural Linking for Text-to-SQL ParsingCode0
Photon: A Robust Cross-Domain Text-to-SQL System—0
Semantic Evaluation for Text-to-SQL with Distilled Test Suite—0
Speak to your Parser: Interactive Text-to-SQL with Natural Language Feedback—0
Dataset and Enhanced Model for Eligibility Criteria-to-SQL Semantic Parsing—0
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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