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
STaR-SQL: Self-Taught Reasoner for Text-to-SQL—0
StatBot.Swiss: Bilingual Open Data Exploration in Natural Language—0
Structured Case-based Reasoning for Inference-time Adaptation of Text-to-SQL parsers—0
Structure-Grounded Pretraining for Text-to-SQL—0
Structure Guided Large Language Model for SQL Generation—0
Structuring the Unstructured: A Multi-Agent System for Extracting and Querying Financial KPIs and Guidance—0
You Only Read Once (YORO): Learning to Internalize Database Knowledge for Text-to-SQL—0
Benchmarking the Text-to-SQL Capability of Large Language Models: A Comprehensive Evaluation—0
SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers—0
SWE-SQL: Illuminating LLM Pathways to Solve User SQL Issues in Real-World Applications—0
Syntactic and Semantic Control of Large Language Models via Sequential Monte Carlo—0
Using LLM to select the right SQL Query from candidates—0
SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-Domain Text-to-SQL Task—0
Understanding the Effect of Algorithm Transparency of Model Explanations in Text-to-SQL Semantic Parsing—0
T5QL: Taming language models for SQL generation—0
Adapt and Decompose: Efficient Generalization of Text-to-SQL via Domain Adapted Least-To-Most Prompting—0
BEAVER: An Enterprise Benchmark for Text-to-SQL—0
Battle of the Large Language Models: Dolly vs LLaMA vs Vicuna vs Guanaco vs Bard vs ChatGPT -- A Text-to-SQL Parsing Comparison—0
Balancing Content Size in RAG-Text2SQL System—0
TARGET: Benchmarking Table Retrieval for Generative Tasks—0
Awakening Latent Grounding from Pretrained Language Models for Semantic Parsing—0
V-SQL: A View-based Two-stage Text-to-SQL Framework—0
A System and Benchmark for LLM-based Q&A on Heterogeneous Data—0
Text-to-SQL based on Large Language Models and Database Keyword Search—0
Text-to-SQL Calibration: No Need to Ask -- Just Rescale Model Probabilities—0
GenEdit: Compounding Operators and Continuous Improvement to Tackle Text-to-SQL in the Enterprise—0
Genicious: Contextual Few-shot Prompting for Insights Discovery—0
Synthesizing Text-to-SQL Data from Weak and Strong LLMs—0
GP: Context-free Grammar Pre-training for Text-to-SQL Parsers—0
Grammar-based Neural Text-to-SQL Generation—0
Graph Enhanced Cross-Domain Text-to-SQL Generation—0
A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directions—0
Graph Neural Networks for Databases: A Survey—0
From Natural Language to SQL: Review of LLM-based Text-to-SQL Systems—0
FinSQL: Model-Agnostic LLMs-based Text-to-SQL Framework for Financial Analysis—0
Faster and Better Grammar-based Text-to-SQL Parsing via Clause-level Parallel Decoding and Alignment Loss—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
Fact-Consistency Evaluation of Text-to-SQL Generation for Business Intelligence Using Exaone 3.5—0
EzSQL: An SQL intermediate representation for improving SQL-to-text Generation—0
Extractive Schema Linking for Text-to-SQL—0
Importance of Synthesizing High-quality Data for Text-to-SQL Parsing—0
A Survey on Employing Large Language Models for Text-to-SQL Tasks—0
Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunities—0
Improving Generalization in Semantic Parsing by Increasing Natural Language Variation—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
A Survey of Large Language Model-Based Generative AI for Text-to-SQL: Benchmarks, Applications, Use Cases, and Challenges—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