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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
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
BookSQL: A Large Scale Text-to-SQL Dataset for Accounting DomainCode1
StatBot.Swiss: Bilingual Open Data Exploration in Natural Language—0
Learning to Clarify: Multi-turn Conversations with Action-Based Contrastive Self-Training—0
CHESS: Contextual Harnessing for Efficient SQL SynthesisCode3
Before Generation, Align it! A Novel and Effective Strategy for Mitigating Hallucinations in Text-to-SQL GenerationCode2
EHR-SeqSQL : A Sequential Text-to-SQL Dataset For Interactively Exploring Electronic Health RecordsCode1
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
Overview of the EHRSQL 2024 Shared Task on Reliable Text-to-SQL Modeling on Electronic Health RecordsCode2
Open-SQL Framework: Enhancing Text-to-SQL on Open-source Large Language Models—0
CoE-SQL: In-Context Learning for Multi-Turn Text-to-SQL with Chain-of-EditionsCode1
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
Dubo-SQL: Diverse Retrieval-Augmented Generation and Fine Tuning for Text-to-SQLCode1
Demonstration of DB-GPT: Next Generation Data Interaction System Empowered by Large Language ModelsCode11
Towards Compositionally Generalizable Semantic Parsing in Large Language Models: A Survey—0
TabSQLify: Enhancing Reasoning Capabilities of LLMs Through Table DecompositionCode1
On Linearizing Structured Data in Encoder-Decoder Language Models: Insights from Text-to-SQL—0
TrustSQL: Benchmarking Text-to-SQL Reliability with Penalty-Based ScoringCode0
Retrieval augmented text-to-SQL generation for epidemiological question answering using electronic health recordsCode1
PET-SQL: A Prompt-Enhanced Two-Round Refinement of Text-to-SQL with Cross-consistencyCode2
Schema-Aware Multi-Task Learning for Complex Text-to-SQL—0
Benchmarking the Text-to-SQL Capability of Large Language Models: A Comprehensive Evaluation—0
DFIN-SQL: Integrating Focused Schema with DIN-SQL for Superior Accuracy in Large-Scale Databases—0
CodeS: Towards Building Open-source Language Models for Text-to-SQLCode2
Ar-Spider: Text-to-SQL in Arabic—0
R^3: "This is My SQL, Are You With Me?" A Consensus-Based Multi-Agent System for Text-to-SQL Tasks—0
Structure Guided Large Language Model for SQL Generation—0
Archer: A Human-Labeled Text-to-SQL Dataset with Arithmetic, Commonsense and Hypothetical Reasoning—0
Understanding the Effects of Noise in Text-to-SQL: An Examination of the BIRD-Bench BenchmarkCode1
Knowledge-to-SQL: Enhancing SQL Generation with Data Expert LLMCode0
Decomposition for Enhancing Attention: Improving LLM-based Text-to-SQL through Workflow ParadigmCode1
Improving Demonstration Diversity by Human-Free Fusing for Text-to-SQLCode0
MURRE: Multi-Hop Table Retrieval with Removal for Open-Domain Text-to-SQLCode0
When is Tree Search Useful for LLM Planning? It Depends on the DiscriminatorCode2
Improving Generalization in Semantic Parsing by Increasing Natural Language Variation—0
Evaluating the Data Model Robustness of Text-to-SQL Systems Based on Real User QueriesCode0
Investigating the Impact of Data Contamination of Large Language Models in Text-to-SQL Translation—0
AraSpider: Democratizing Arabic-to-SQLCode0
DTS-SQL: Decomposed Text-to-SQL with Small Large Language Models—0
Analyzing the Effectiveness of Large Language Models on Text-to-SQL SynthesisCode1
FinSQL: Model-Agnostic LLMs-based Text-to-SQL Framework for Financial Analysis—0
Using LLM to select the right SQL Query from candidates—0
Semantic Parsing for Complex Data Retrieval: Targeting Query Plans vs. SQL for No-Code Access to Relational Databases—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