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 76–100 of 424 papers

TitleStatusHype
QDA-SQL: Questions Enhanced Dialogue Augmentation for Multi-Turn Text-to-SQLCode1
Know What I don't Know: Handling Ambiguous and Unanswerable Questions for Text-to-SQLCode1
An Investigation Between Schema Linking and Text-to-SQL PerformanceCode1
Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-TrainingCode1
DrugEHRQA: A Question Answering Dataset on Structured and Unstructured Electronic Health Records For Medicine Related QueriesCode1
CRUSH4SQL: Collective Retrieval Using Schema Hallucination For Text2SQLCode1
In-Context Learning for Few-Shot Dialogue State TrackingCode1
Interactive Text-to-SQL Generation via Editable Step-by-Step ExplanationsCode1
Dr.Spider: A Diagnostic Evaluation Benchmark towards Text-to-SQL RobustnessCode1
Learning to Simulate Natural Language Feedback for Interactive Semantic ParsingCode1
MAGIC: Generating Self-Correction Guideline for In-Context Text-to-SQLCode1
Hybrid Ranking Network for Text-to-SQLCode1
DuoRAT: Towards Simpler Text-to-SQL ModelsCode1
MSc-SQL: Multi-Sample Critiquing Small Language Models For Text-To-SQL TranslationCode1
An Imitation Game for Learning Semantic Parsers from User InteractionCode1
IGSQL: Database Schema Interaction Graph Based Neural Model for Context-Dependent Text-to-SQL GenerationCode1
ACT-SQL: In-Context Learning for Text-to-SQL with Automatically-Generated Chain-of-ThoughtCode1
Augmenting Multi-Turn Text-to-SQL Datasets with Self-PlayCode1
Decomposition for Enhancing Attention: Improving LLM-based Text-to-SQL through Workflow ParadigmCode1
Improving Generalization in Language Model-Based Text-to-SQL Semantic Parsing: Two Simple Semantic Boundary-Based TechniquesCode1
Learning to Synthesize Data for Semantic ParsingCode1
FLEX: Expert-level False-Less EXecution Metric for Reliable Text-to-SQL BenchmarkCode1
GraPPa: Grammar-Augmented Pre-Training for Table Semantic ParsingCode1
CoE-SQL: In-Context Learning for Multi-Turn Text-to-SQL with Chain-of-EditionsCode1
ExeSQL: Self-Taught Text-to-SQL Models with Execution-Driven Bootstrapping for SQL DialectsCode1
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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