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 301–350 of 424 papers

TitleStatusHype
SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers—0
SWE-SQL: Illuminating LLM Pathways to Solve User SQL Issues in Real-World Applications—0
Syntactic and Semantic Control of Large Language Models via Sequential Monte Carlo—0
SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-Domain Text-to-SQL Task—0
T5QL: Taming language models for SQL generation—0
TARGET: Benchmarking Table Retrieval for Generative Tasks—0
Text-to-SQL based on Large Language Models and Database Keyword Search—0
Text-to-SQL Calibration: No Need to Ask -- Just Rescale Model Probabilities—0
The Death of Schema Linking? Text-to-SQL in the Age of Well-Reasoned Language Models—0
The Role of Accuracy and Validation Effectiveness in Conversational Business Analytics—0
TinySQL: A Progressive Text-to-SQL Dataset for Mechanistic Interpretability Research—0
Tool-Assisted Agent on SQL Inspection and Refinement in Real-World Scenarios—0
Towards Compositionally Generalizable Semantic Parsing in Large Language Models: A Survey—0
Towards Generalizable and Robust Text-to-SQL Parsing—0
Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge—0
Towards Optimizing SQL Generation via LLM Routing—0
Towards Robustness of Text-to-SQL Models Against Natural and Realistic Adversarial Table Perturbation—0
Towards Robustness of Text-to-SQL Models Against Natural and Realistic Adversarial Table Perturbation—0
Towards Understanding the Generalization of Medical Text-to-SQL Models and Datasets—0
Turing: an Accurate and Interpretable Multi-Hypothesis Cross-Domain Natural Language Database Interface—0
UniSAr: A Unified Structure-Aware Autoregressive Language Model for Text-to-SQL—0
UniSAr: A Unified Structure-Aware Autoregressive Language Model for Text-to-SQL—0
UNJOIN: Enhancing Multi-Table Text-to-SQL Generation via Schema Simplification—0
Unmasking Database Vulnerabilities: Zero-Knowledge Schema Inference Attacks in Text-to-SQL Systems—0
Using LLM to select the right SQL Query from candidates—0
V-SQL: A View-based Two-stage Text-to-SQL Framework—0
Weakly Supervised Text-to-SQL Parsing through Question Decomposition—0
"What Do You Mean by That?" A Parser-Independent Interactive Approach for Enhancing Text-to-SQL—0
``What Do You Mean by That?'' A Parser-Independent Interactive Approach for Enhancing Text-to-SQL—0
You Only Read Once (YORO): Learning to Internalize Database Knowledge for Text-to-SQL—0
On Linearizing Structured Data in Encoder-Decoder Language Models: Insights from Text-to-SQL—0
On the Security Vulnerabilities of Text-to-SQL Models—0
On the Structural Generalization in Text-to-SQL—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
Photon: A Robust Cross-Domain Text-to-SQL System—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
Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsing—0
NL-EDIT: Correcting semantic parse errors through natural language interactionCode0
PTD-SQL: Partitioning and Targeted Drilling with LLMs in Text-to-SQLCode0
Text-to-SQL Domain Adaptation via Human-LLM Collaborative Data AnnotationCode0
ColloQL: Robust Text-to-SQL Over Search QueriesCode0
OpenGrok: Enhancing SNS Data Processing with Distilled Knowledge and Mask-like MechanismsCode0
Encoding Database Schemas with Relation-Aware Self-Attention for Text-to-SQL ParsersCode0
ColloQL: Robust Cross-Domain Text-to-SQL Over Search QueriesCode0
MURRE: Multi-Hop Table Retrieval with Removal for Open-Domain Text-to-SQLCode0
MAGNIFICo: Evaluating the In-Context Learning Ability of Large Language Models to Generalize to Novel InterpretationsCode0
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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