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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 151–200 of 424 papers

TitleStatusHype
JOLT-SQL: Joint Loss Tuning of Text-to-SQL with Confusion-aware Noisy Schema SamplingCode0
Bertrand-DR: Improving Text-to-SQL using a Discriminative Re-rankerCode0
Semi-Automatic Construction of Text-to-SQL Data for Domain TransferCode0
SelECT-SQL: Self-correcting ensemble Chain-of-Thought for Text-to-SQLCode0
Selective Demonstrations for Cross-domain Text-to-SQLCode0
Semantic Decomposition of Question and SQL for Text-to-SQL ParsingCode0
Benchmarking and Improving Text-to-SQL Generation under AmbiguityCode0
Improving Text-to-SQL Evaluation MethodologyCode0
AraSpider: Democratizing Arabic-to-SQLCode0
DataGpt-SQL-7B: An Open-Source Language Model for Text-to-SQLCode0
Improving Generalization in Semantic Parsing by Increasing Natural Language VariationCode0
Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL ParsingCode0
Representing Schema Structure with Graph Neural Networks for Text-to-SQL ParsingCode0
DAC: Decomposed Automation Correction for Text-to-SQLCode0
CSS: A Large-scale Cross-schema Chinese Text-to-SQL Medical DatasetCode0
Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL ParsingCode0
CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to DatabasesCode0
Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL ParsingCode0
PG-GSQL: Pointer-Generator Network with Guide Decoding for Cross-Domain Context-Dependent Text-to-SQL GenerationCode0
ProbGate at EHRSQL 2024: Enhancing SQL Query Generation Accuracy through Probabilistic Threshold Filtering and Error HandlingCode0
Robust Text-to-SQL Generation with Execution-Guided DecodingCode0
ShadowGNN: Graph Projection Neural Network for Text-to-SQL ParserCode0
Correcting Semantic Parses with Natural Language through Dynamic Schema EncodingCode0
Improving Demonstration Diversity by Human-Free Fusing for Text-to-SQLCode0
Global Reasoning over Database Structures for Text-to-SQL ParsingCode0
NL-EDIT: Correcting semantic parse errors through natural language interactionCode0
OpenGrok: Enhancing SNS Data Processing with Distilled Knowledge and Mask-like MechanismsCode0
Awakening Latent Grounding from Pretrained Language Models for Semantic ParsingCode0
MultiSpider: Towards Benchmarking Multilingual Text-to-SQL Semantic ParsingCode0
Federated Learning for Semantic Parsing: Task Formulation, Evaluation Setup, New AlgorithmsCode0
MURRE: Multi-Hop Table Retrieval with Removal for Open-Domain Text-to-SQLCode0
Content Enhanced BERT-based Text-to-SQL GenerationCode0
ColloQL: Robust Text-to-SQL Over Search QueriesCode0
Exploring Underexplored Limitations of Cross-Domain Text-to-SQL GeneralizationCode0
I Need Help! Evaluating LLM's Ability to Ask for Users' Support: A Case Study on Text-to-SQL GenerationCode0
Integrating question answering and text-to-SQL in PortugueseCode0
Confidence Estimation for Error Detection in Text-to-SQL SystemsCode0
Automated Self-Refinement and Self-Correction for LLM-based Product Attribute Value ExtractionCode0
MAGNIFICo: Evaluating the In-Context Learning Ability of Large Language Models to Generalize to Novel InterpretationsCode0
ColloQL: Robust Cross-Domain Text-to-SQL Over Search QueriesCode0
Explainable Multi-Modal Data Exploration in Natural Language via LLM AgentCode0
AnDB: Breaking Boundaries with an AI-Native Database for Universal Semantic AnalysisCode0
Text-to-SQL Generation for Question Answering on Electronic Medical RecordsCode0
LLM-SQL-Solver: Can LLMs Determine SQL Equivalence?Code0
Evaluating the Data Model Robustness of Text-to-SQL Systems Based on Real User QueriesCode0
A Tale of Two Linkings: Dynamically Gating between Schema Linking and Structural Linking for Text-to-SQL ParsingCode0
LogicCat: A Chain-of-Thought Text-to-SQL Benchmark for Multi-Domain Reasoning ChallengesCode0
Large Language Models are Good Multi-lingual Learners : When LLMs Meet Cross-lingual PromptsCode0
Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question TypesCode0
ESM+: Modern Insights into Perspective on Text-to-SQL Evaluation in the Age of Large Language ModelsCode0
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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