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 101–150 of 424 papers

TitleStatusHype
Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic ParsingCode1
Did You Ask a Good Question? A Cross-Domain Question Intention Classification Benchmark for Text-to-SQLCode1
mRAT-SQL+GAP:A Portuguese Text-to-SQL TransformerCode1
CoE-SQL: In-Context Learning for Multi-Turn Text-to-SQL with Chain-of-EditionsCode1
Measuring and Improving Compositional Generalization in Text-to-SQL via Component AlignmentCode1
MSc-SQL: Multi-Sample Critiquing Small Language Models For Text-To-SQL TranslationCode1
LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local RelationsCode1
ACT-SQL: In-Context Learning for Text-to-SQL with Automatically-Generated Chain-of-ThoughtCode1
FLEX: Expert-level False-Less EXecution Metric for Reliable Text-to-SQL BenchmarkCode1
Dr.Spider: A Diagnostic Evaluation Benchmark towards Text-to-SQL RobustnessCode1
LEVER: Learning to Verify Language-to-Code Generation with ExecutionCode1
Learning to Synthesize Data for Semantic ParsingCode1
Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-TrainingCode1
Dubo-SQL: Diverse Retrieval-Augmented Generation and Fine Tuning for Text-to-SQLCode1
Leveraging Table Content for Zero-shot Text-to-SQL with Meta-LearningCode1
Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLsCode1
MAGIC: Generating Self-Correction Guideline for In-Context Text-to-SQLCode1
Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?Code1
PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language ModelsCode1
Semantic Enhanced Text-to-SQL Parsing via Iteratively Learning Schema Linking GraphCode1
Towards Automated Cross-domain Exploratory Data Analysis through Large Language ModelsCode1
Enhancing Few-shot Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategies—0
End-to-end Text-to-SQL Generation within an Analytics Insight Engine—0
CHASE-SQL: Multi-Path Reasoning and Preference Optimized Candidate Selection in Text-to-SQL—0
End-to-End Cross-Domain Text-to-SQL Semantic Parsing with Auxiliary Task—0
Chase: A Large-Scale and Pragmatic Chinese Dataset for Cross-Database Context-Dependent Text-to-SQL—0
A Survey on Employing Large Language Models for Text-to-SQL Tasks—0
EllieSQL: Cost-Efficient Text-to-SQL with Complexity-Aware Routing—0
CEGI: Measuring the trade-off between efficiency and carbon emissions for SLMs and VLMs—0
CCoE: A Compact LLM with Collaboration of Experts—0
DuSQL: A Large-Scale and Pragmatic Chinese Text-to-SQL Dataset—0
A Survey of Large Language Model-Based Generative AI for Text-to-SQL: Benchmarks, Applications, Use Cases, and Challenges—0
DTS-SQL: Decomposed Text-to-SQL with Small Large Language Models—0
DrugEHRQA: A Question Answering Dataset on Structured and Unstructured Electronic Health Records For Medicine Related Queries—0
Calibrating LLMs for Text-to-SQL Parsing by Leveraging Sub-clause Frequencies—0
An Actor-Critic Approach to Boosting Text-to-SQL Large Language Model—0
Investigating the Impact of Data Contamination of Large Language Models in Text-to-SQL Translation—0
Domain Adaptation of a State of the Art Text-to-SQL Model: Lessons Learned and Challenges Found—0
ASTormer: An AST Structure-aware Transformer Decoder for Text-to-SQL—0
DocuT5: Seq2seq SQL Generation with Table Documentation—0
Divide and Prompt: Chain of Thought Prompting for Text-to-SQL—0
Bridging the Generalization Gap in Text-to-SQL Parsing with Schema Expansion—0
Increasing the LLM Accuracy for Question Answering: Ontologies to the Rescue!—0
Diverse Parallel Data Synthesis for Cross-Database Adaptation of Text-to-SQL Parsers—0
Bridging the Gap: Transforming Natural Language Questions into SQL Queries via Abstract Query Pattern and Contextual Schema Markup—0
Ambiguity Resolution in Text-to-Structured Data Mapping—0
dIR -- Discrete Information Retrieval: Conversational Search over Unstructured (and Structured) Data with Large Language Models—0
Ar-Spider: Text-to-SQL in Arabic—0
IncSQL: Training Incremental Text-to-SQL Parsers with Non-Deterministic Oracles—0
Dialect2SQL: A Novel Text-to-SQL Dataset for Arabic Dialects with a Focus on Moroccan Darija—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