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 251–300 of 424 papers

TitleStatusHype
On the Structural Generalization in Text-to-SQL—0
Towards Understanding the Generalization of Medical Text-to-SQL Models and Datasets—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
dIR -- Discrete Information Retrieval: Conversational Search over Unstructured (and Structured) Data with Large Language Models—0
Dialect2SQL: A Novel Text-to-SQL Dataset for Arabic Dialects with a Focus on Moroccan Darija—0
Prompting GPT-3.5 for Text-to-SQL with De-semanticization and Skeleton Retrieval—0
DFIN-SQL: Integrating Focused Schema with DIN-SQL for Superior Accuracy in Large-Scale Databases—0
Weakly Supervised Text-to-SQL Parsing through Question Decomposition—0
Photon: A Robust Cross-Domain Text-to-SQL System—0
Deep Learning Driven Natural Languages Text to SQL Query Conversion: A Survey—0
Pi-SQL: Enhancing Text-to-SQL with Fine-Grained Guidance from Pivot Programming Languages—0
PRACTIQ: A Practical Conversational Text-to-SQL dataset with Ambiguous and Unanswerable Queries—0
Prefix-to-SQL: Text-to-SQL Generation from Incomplete User Questions—0
Pretrained Language Models Are All You Need For Text-to-SQL Schema Linking—0
One-Shot Learning for Text-to-SQL Generation—0
Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsing—0
PromptMind Team at EHRSQL-2024: Improving Reliability of SQL Generation using Ensemble LLMs—0
"What Do You Mean by That?" A Parser-Independent Interactive Approach for Enhancing Text-to-SQL—0
PSM-SQL: Progressive Schema Learning with Multi-granularity Semantics for Text-to-SQL—0
Decoupling SQL Query Hardness Parsing for Text-to-SQL—0
Query and Conquer: Execution-Guided SQL Generation—0
QURG: Question Rewriting Guided Context-Dependent Text-to-SQL Semantic Parsing—0
Decoupled Dialogue Modeling and Semantic Parsing for Multi-Turn Text-to-SQL—0
Rationalization Models for Text-to-SQL—0
DCG-SQL: Enhancing In-Context Learning for Text-to-SQL with Deep Contextual Schema Link Graph—0
RB-SQL: A Retrieval-based LLM Framework for Text-to-SQL—0
Reasoning-SQL: Reinforcement Learning with SQL Tailored Partial Rewards for Reasoning-Enhanced Text-to-SQL—0
DB-Explore: Automated Database Exploration and Instruction Synthesis for Text-to-SQL—0
Reboost Large Language Model-based Text-to-SQL, Text-to-Python, and Text-to-Function -- with Real Applications in Traffic Domain—0
Data Transformation to Construct a Dataset for Generating Entity-Relationship Model from Natural Language—0
Clause-Wise and Recursive Decoding for Complex and Cross-Domain Text-to-SQL Generation—0
Re-examining the Role of Schema Linking in Text-to-SQL—0
ReEx-SQL: Reasoning with Execution-Aware Reinforcement Learning for Text-to-SQL—0
ReFoRCE: A Text-to-SQL Agent with Self-Refinement, Format Restriction, and Column Exploration—0
Reliable Text-to-SQL with Adaptive Abstention—0
Turing: an Accurate and Interpretable Multi-Hypothesis Cross-Domain Natural Language Database Interface—0
Retrieval-augmented GPT-3.5-based Text-to-SQL Framework with Sample-aware Prompting and Dynamic Revision Chain—0
Dataset and Enhanced Model for Eligibility Criteria-to-SQL Semantic Parsing—0
Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsing—0
RH-SQL: Refined Schema and Hardness Prompt for Text-to-SQL—0
``What Do You Mean by That?'' A Parser-Independent Interactive Approach for Enhancing Text-to-SQL—0
Data-Anonymous Encoding for Text-to-SQL Generation—0
CQR-SQL: Conversational Question Reformulation Enhanced Context-Dependent Text-to-SQL Parsers—0
Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents—0
S^2SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers—0
S^2SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers—0
S^2SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers—0
Conversational Text-to-SQL: An Odyssey into State-of-the-Art and Challenges Ahead—0
SAFE-SQL: Self-Augmented In-Context Learning with Fine-grained Example Selection for 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