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 201–250 of 424 papers

TitleStatusHype
Graph Enhanced Cross-Domain Text-to-SQL Generation—0
Graph Neural Networks for Databases: A Survey—0
Hierarchical Neural Data Synthesis for Semantic Parsing—0
HIE-SQL: History Information Enhanced Network for Context-Dependent Text-to-SQL Semantic Parsing—0
HIE-SQL: History Information Enhanced Network for Context-Dependent Text-to-SQL Semantic Parsing—0
HI-SQL: Optimizing Text-to-SQL Systems through Dynamic Hint Integration—0
Importance of Synthesizing High-quality Data for Text-to-SQL Parsing—0
Synthetic SQL Column Descriptions and Their Impact on Text-to-SQL Performance—0
Improving Retrieval-augmented Text-to-SQL with AST-based Ranking and Schema Pruning—0
Improving Text-to-SQL Semantic Parsing with Fine-grained Query Understanding—0
Improving Text-to-SQL with Schema Dependency Learning—0
Increasing the LLM Accuracy for Question Answering: Ontologies to the Rescue!—0
IncSQL: Training Incremental Text-to-SQL Parsers with Non-Deterministic Oracles—0
Interactive-T2S: Multi-Turn Interactions for Text-to-SQL with Large Language Models—0
Investigating the Impact of Data Contamination of Large Language Models in Text-to-SQL Translation—0
KeyInst: Keyword Instruction for Improving SQL Formulation in Text-to-SQL—0
Knapsack Optimization-based Schema Linking for LLM-based Text-to-SQL Generation—0
Knowledge Base Question Answering: A Semantic Parsing Perspective—0
KU-DMIS at EHRSQL 2024:Generating SQL query via question templatization in EHR—0
Laziness Is a Virtue When It Comes to Compositionality in Neural Semantic Parsing—0
Learning by Distilling Context—0
Learning from Imperfect Data: Towards Efficient Knowledge Distillation of Autoregressive Language Models for Text-to-SQL—0
Learning Metadata-Agnostic Representations for Text-to-SQL In-Context Example Selection—0
Learning to Clarify: Multi-turn Conversations with Action-Based Contrastive Self-Training—0
LEDD: Large Language Model-Empowered Data Discovery in Data Lakes—0
Leveraging Adjective-Noun Phrasing Knowledge for Comparison Relation Prediction in Text-to-SQL—0
Leveraging Explicit Lexico-logical Alignments in Text-to-SQL Parsing—0
LG AI Research & KAIST at EHRSQL 2024: Self-Training Large Language Models with Pseudo-Labeled Unanswerable Questions for a Reliable Text-to-SQL System on EHRs—0
LLM-Driven Data Generation and a Novel Soft Metric for Evaluating Text-to-SQL in Aviation MRO—0
LLM-Powered Agents for Navigating Venice's Historical Cadastre—0
Lucy: Think and Reason to Solve Text-to-SQL—0
Makadi: A Large-Scale Human-Labeled Dataset for Hindi Semantic Parsing—0
Making LLMs Work for Enterprise Data Tasks—0
MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generation—0
MCTS-SQL: An Effective Framework for Text-to-SQL with Monte Carlo Tree Search—0
Measuring and Improving Compositional Generalization in Text-to-SQL via Component Alignment—0
Mention Extraction and Linking for SQL Query Generation—0
Meta-aware Learning in text-to-SQL Large Language Model—0
MIGA: A Unified Multi-task Generation Framework for Conversational Text-to-SQL—0
MT-Teql: Evaluating and Augmenting Consistency of Text-to-SQL Models with Metamorphic Testing—0
Natural Language Interfaces for Tabular Data Querying and Visualization: A Survey—0
N-Best Hypotheses Reranking for Text-To-SQL Systems—0
Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQL—0
On Linearizing Structured Data in Encoder-Decoder Language Models: Insights from Text-to-SQL—0
On the Security Vulnerabilities of Text-to-SQL Models—0
On the Structural Generalization in Text-to-SQL—0
OpenSearch-SQL: Enhancing Text-to-SQL with Dynamic Few-shot and Consistency Alignment—0
Open-SQL Framework: Enhancing Text-to-SQL on Open-source Large Language Models—0
Photon: A Robust Cross-Domain Text-to-SQL System—0
Pi-SQL: Enhancing Text-to-SQL with Fine-Grained Guidance from Pivot Programming Languages—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