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
QURG: Question Rewriting Guided Context-Dependent Text-to-SQL Semantic Parsing—0
SPSQL: Step-by-step Parsing Based Framework for Text-to-SQL Generation—0
Laziness Is a Virtue When It Comes to Compositionality in Neural Semantic Parsing—0
Controllable Data Augmentation for Context-Dependent Text-to-SQL—0
Prompting GPT-3.5 for Text-to-SQL with De-semanticization and Skeleton Retrieval—0
Divide and Prompt: Chain of Thought Prompting for Text-to-SQL—0
Teaching Large Language Models to Self-DebugCode0
Towards Understanding the Generalization of Medical Text-to-SQL Models and Datasets—0
Conversational Text-to-SQL: An Odyssey into State-of-the-Art and Challenges Ahead—0
Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL ParsingCode0
On the Structural Generalization in Text-to-SQL—0
Structured Case-based Reasoning for Inference-time Adaptation of Text-to-SQL parsers—0
Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge—0
MultiSpider: Towards Benchmarking Multilingual Text-to-SQL Semantic Parsing—0
Towards Robustness of Text-to-SQL Models Against Natural and Realistic Adversarial Table Perturbation—0
MIGA: A Unified Multi-task Generation Framework for Conversational Text-to-SQL—0
Importance of Synthesizing High-quality Data for Text-to-SQL Parsing—0
On the Security Vulnerabilities of Text-to-SQL Models—0
Learn from Yesterday: A Semi-Supervised Continual Learning Method for Supervision-Limited Text-to-SQL Task StreamsCode0
DocuT5: Seq2seq SQL Generation with Table Documentation—0
Diverse Parallel Data Synthesis for Cross-Database Adaptation of Text-to-SQL Parsers—0
XRICL: Cross-lingual Retrieval-Augmented In-Context Learning for Cross-lingual Text-to-SQL Semantic ParsingCode0
Towards Generalizable and Robust Text-to-SQL Parsing—0
STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing—0
N-Best Hypotheses Reranking for Text-To-SQL Systems—0
Addressing Limitations of Encoder-Decoder Based Approach to Text-to-SQL—0
Learning by Distilling Context—0
Improving Text-to-SQL Semantic Parsing with Fine-grained Query Understanding—0
T5QL: Taming language models for SQL generation—0
SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers—0
Knowledge Base Question Answering: A Semantic Parsing Perspective—0
A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directions—0
SeSQL: Yet Another Large-scale Session-level Chinese Text-to-SQL Dataset—0
Deep Learning Driven Natural Languages Text to SQL Query Conversion: A Survey—0
Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL ParsingCode0
Makadi: A Large-Scale Human-Labeled Dataset for Hindi Semantic Parsing—0
Non-Programmers Can Label Programs Indirectly via Active Examples: A Case Study with Text-to-SQLCode0
CQR-SQL: Conversational Question Reformulation Enhanced Context-Dependent Text-to-SQL Parsers—0
Bridging the Generalization Gap in Text-to-SQL Parsing with Schema Expansion—0
Leveraging Explicit Lexico-logical Alignments in Text-to-SQL Parsing—0
S^2SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers—0
Faster and Better Grammar-based Text-to-SQL Parsing via Clause-level Parallel Decoding and Alignment Loss—0
UniSAr: A Unified Structure-Aware Autoregressive Language Model for Text-to-SQL—0
Evaluating the Text-to-SQL Capabilities of Large Language Models—0
HIE-SQL: History Information Enhanced Network for Context-Dependent Text-to-SQL Semantic Parsing—0
S^2SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers—0
Integrating question answering and text-to-SQL in PortugueseCode0
Weakly Supervised Text-to-SQL Parsing through Question Decomposition—0
Exploring Example Selection for Few-shot Text-to-SQL Semantic Parsing—0
Speech-to-SQL: Towards Speech-driven SQL Query Generation From Natural Language Question—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