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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 201–250 of 424 papers

TitleStatusHype
Learning Metadata-Agnostic Representations for Text-to-SQL In-Context Example Selection—0
Learning from Imperfect Data: Towards Efficient Knowledge Distillation of Autoregressive Language Models for Text-to-SQL—0
PRACTIQ: A Practical Conversational Text-to-SQL dataset with Ambiguous and Unanswerable Queries—0
Understanding the Effect of Algorithm Transparency of Model Explanations in Text-to-SQL Semantic Parsing—0
CHASE-SQL: Multi-Path Reasoning and Preference Optimized Candidate Selection in Text-to-SQL—0
From Natural Language to SQL: Review of LLM-based Text-to-SQL Systems—0
SynTQA: Synergistic Table-based Question Answering via Mixture of Text-to-SQL and E2E TQACode0
DataGpt-SQL-7B: An Open-Source Language Model for Text-to-SQLCode0
Enhancing Text-to-SQL Capabilities of Large Language Models via Domain Database Knowledge Injection—0
PTD-SQL: Partitioning and Targeted Drilling with LLMs in Text-to-SQLCode0
You Only Read Once (YORO): Learning to Internalize Database Knowledge for Text-to-SQL—0
Large Language Models are Good Multi-lingual Learners : When LLMs Meet Cross-lingual PromptsCode0
SelECT-SQL: Self-correcting ensemble Chain-of-Thought for Text-to-SQLCode0
A System and Benchmark for LLM-based Q&A on Heterogeneous Data—0
BEAVER: An Enterprise Benchmark for Text-to-SQL—0
Tool-Assisted Agent on SQL Inspection and Refinement in Real-World Scenarios—0
SQL-GEN: Bridging the Dialect Gap for Text-to-SQL Via Synthetic Data And Model Merging—0
DAC: Decomposed Automation Correction for Text-to-SQLCode0
The Death of Schema Linking? Text-to-SQL in the Age of Well-Reasoned Language Models—0
Interactive-T2S: Multi-Turn Interactions for Text-to-SQL with Large Language Models—0
Synthetic SQL Column Descriptions and Their Impact on Text-to-SQL Performance—0
Synthesizing Text-to-SQL Data from Weak and Strong LLMs—0
Evaluating LLMs for Text-to-SQL Generation With Complex SQL Workload—0
Making LLMs Work for Enterprise Data Tasks—0
A Survey on Employing Large Language Models for Text-to-SQL Tasks—0
I Need Help! Evaluating LLM's Ability to Ask for Users' Support: A Case Study on Text-to-SQL GenerationCode0
SQLfuse: Enhancing Text-to-SQL Performance through Comprehensive LLM Synergy—0
CCoE: A Compact LLM with Collaboration of Experts—0
RB-SQL: A Retrieval-based LLM Framework for Text-to-SQL—0
ESM+: Modern Insights into Perspective on Text-to-SQL Evaluation in the Age of Large Language ModelsCode0
Lucy: Think and Reason to Solve Text-to-SQL—0
Improving Retrieval-augmented Text-to-SQL with AST-based Ranking and Schema Pruning—0
Beyond Text-to-SQL for IoT Defense: A Comprehensive Framework for Querying and Classifying IoT Threats—0
Unmasking Database Vulnerabilities: Zero-Knowledge Schema Inference Attacks in Text-to-SQL Systems—0
End-to-end Text-to-SQL Generation within an Analytics Insight Engine—0
RH-SQL: Refined Schema and Hardness Prompt for Text-to-SQL—0
Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQL—0
StatBot.Swiss: Bilingual Open Data Exploration in Natural Language—0
Learning to Clarify: Multi-turn Conversations with Action-Based Contrastive Self-Training—0
KU-DMIS at EHRSQL 2024:Generating SQL query via question templatization in EHR—0
Increasing the LLM Accuracy for Question Answering: Ontologies to the Rescue!—0
LG AI Research & KAIST at EHRSQL 2024: Self-Training Large Language Models with Pseudo-Labeled Unanswerable Questions for a Reliable Text-to-SQL System on EHRs—0
SQL-to-Schema Enhances Schema Linking in Text-to-SQL—0
PromptMind Team at EHRSQL-2024: Improving Reliability of SQL Generation using Ensemble LLMs—0
MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generation—0
Open-SQL Framework: Enhancing Text-to-SQL on Open-source Large Language Models—0
ProbGate at EHRSQL 2024: Enhancing SQL Query Generation Accuracy through Probabilistic Threshold Filtering and Error HandlingCode0
EPI-SQL: Enhancing Text-to-SQL Translation with Error-Prevention Instructions—0
Towards Compositionally Generalizable Semantic Parsing in Large Language Models: A Survey—0
On Linearizing Structured Data in Encoder-Decoder Language Models: Insights from 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