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 151–200 of 424 papers

TitleStatusHype
Syntactic and Semantic Control of Large Language Models via Sequential Monte Carlo—0
Sigma: A dataset for text-to-code semantic parsing with statistical analysisCode0
Query and Conquer: Execution-Guided SQL Generation—0
Reasoning-SQL: Reinforcement Learning with SQL Tailored Partial Rewards for Reasoning-Enhanced Text-to-SQL—0
EllieSQL: Cost-Efficient Text-to-SQL with Complexity-Aware Routing—0
GenEdit: Compounding Operators and Continuous Improvement to Tackle Text-to-SQL in the Enterprise—0
Valid Text-to-SQL Generation with Unification-based DeepStochLogCode0
TinySQL: A Progressive Text-to-SQL Dataset for Mechanistic Interpretability Research—0
Genicious: Contextual Few-shot Prompting for Insights Discovery—0
SQLCritic: Correcting Text-to-SQL Generation via Clause-wise Critic—0
DB-Explore: Automated Database Exploration and Instruction Synthesis for Text-to-SQL—0
Disambiguate First Parse Later: Generating Interpretations for Ambiguity Resolution in Semantic ParsingCode0
LEDD: Large Language Model-Empowered Data Discovery in Data Lakes—0
Text-to-SQL Domain Adaptation via Human-LLM Collaborative Data AnnotationCode0
Bridging the Gap: Transforming Natural Language Questions into SQL Queries via Abstract Query Pattern and Contextual Schema Markup—0
OpenSearch-SQL: Enhancing Text-to-SQL with Dynamic Few-shot and Consistency Alignment—0
STaR-SQL: Self-Taught Reasoner for Text-to-SQL—0
AnDB: Breaking Boundaries with an AI-Native Database for Universal Semantic AnalysisCode0
Knapsack Optimization-based Schema Linking for LLM-based Text-to-SQL Generation—0
Graph Neural Networks for Databases: A Survey—0
SAFE-SQL: Self-Augmented In-Context Learning with Fine-grained Example Selection for Text-to-SQL—0
OpenGrok: Enhancing SNS Data Processing with Distilled Knowledge and Mask-like MechanismsCode0
Rationalization Models for Text-to-SQL—0
PSM-SQL: Progressive Schema Learning with Multi-granularity Semantics for Text-to-SQL—0
ReFoRCE: A Text-to-SQL Agent with Self-Refinement, Format Restriction, and Column Exploration—0
CoddLLM: Empowering Large Language Models for Data Analytics—0
Balancing Content Size in RAG-Text2SQL System—0
MCTS-SQL: An Effective Framework for Text-to-SQL with Monte Carlo Tree Search—0
Text-to-SQL based on Large Language Models and Database Keyword Search—0
Extractive Schema Linking for Text-to-SQL—0
Dialect2SQL: A Novel Text-to-SQL Dataset for Arabic Dialects with a Focus on Moroccan Darija—0
Reliable Text-to-SQL with Adaptive Abstention—0
Confidence Estimation for Error Detection in Text-to-SQL SystemsCode0
Automated Self-Refinement and Self-Correction for LLM-based Product Attribute Value ExtractionCode0
Explainable Multi-Modal Data Exploration in Natural Language via LLM AgentCode0
Contrato360 2.0: A Document and Database-Driven Question-Answer System using Large Language Models and Agents—0
Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question TypesCode0
Solid-SQL: Enhanced Schema-linking based In-context Learning for Robust Text-to-SQL—0
V-SQL: A View-based Two-stage Text-to-SQL Framework—0
Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents—0
A Survey of Large Language Model-Based Generative AI for Text-to-SQL: Benchmarks, Applications, Use Cases, and Challenges—0
CEGI: Measuring the trade-off between efficiency and carbon emissions for SLMs and VLMs—0
EzSQL: An SQL intermediate representation for improving SQL-to-text Generation—0
Text-to-SQL Calibration: No Need to Ask -- Just Rescale Model Probabilities—0
Leveraging Prior Experience: An Expandable Auxiliary Knowledge Base for Text-to-SQLCode0
The Role of Accuracy and Validation Effectiveness in Conversational Business Analytics—0
Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows—0
Towards Optimizing SQL Generation via LLM Routing—0
An Actor-Critic Approach to Boosting Text-to-SQL Large Language Model—0
KeyInst: Keyword Instruction for Improving SQL Formulation in Text-to-SQL—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