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 1–50 of 424 papers

TitleStatusHype
CogniSQL-R1-Zero: Lightweight Reinforced Reasoning for Efficient SQL Generation—0
XiYan-SQL: A Novel Multi-Generator Framework For Text-to-SQLCode4
SWE-SQL: Illuminating LLM Pathways to Solve User SQL Issues in Real-World Applications—0
Schema-R1: A reasoning training approach for schema linking in Text-to-SQL TaskCode1
HI-SQL: Optimizing Text-to-SQL Systems through Dynamic Hint Integration—0
LLM-Driven Data Generation and a Novel Soft Metric for Evaluating Text-to-SQL in Aviation MRO—0
Bridging the Gap Between Open-Source and Proprietary LLMs in Table QACode0
SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence GenerationCode1
SDE-SQL: Enhancing Text-to-SQL Generation in Large Language Models via Self-Driven Exploration with SQL Probes—0
SQLens: An End-to-End Framework for Error Detection and Correction in Text-to-SQL—0
Reasoning-Table: Exploring Reinforcement Learning for Table ReasoningCode2
Pi-SQL: Enhancing Text-to-SQL with Fine-Grained Guidance from Pivot Programming Languages—0
SHARE: An SLM-based Hierarchical Action CorREction Assistant for Text-to-SQL—0
Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunities—0
Calibrating LLMs for Text-to-SQL Parsing by Leveraging Sub-clause Frequencies—0
DCG-SQL: Enhancing In-Context Learning for Text-to-SQL with Deep Contextual Schema Link Graph—0
Structuring the Unstructured: A Multi-Agent System for Extracting and Querying Financial KPIs and Guidance—0
Meta-aware Learning in text-to-SQL Large Language Model—0
LogicCat: A Chain-of-Thought Text-to-SQL Benchmark for Multi-Domain Reasoning ChallengesCode0
BiomedSQL: Text-to-SQL for Scientific Reasoning on Biomedical Knowledge BasesCode0
UNJOIN: Enhancing Multi-Table Text-to-SQL Generation via Schema Simplification—0
SchemaGraphSQL: Efficient Schema Linking with Pathfinding Graph Algorithms for Text-to-SQL on Large-Scale Databases—0
Arctic-Text2SQL-R1: Simple Rewards, Strong Reasoning in Text-to-SQLCode3
ExeSQL: Self-Taught Text-to-SQL Models with Execution-Driven Bootstrapping for SQL DialectsCode1
LLM-Powered Agents for Navigating Venice's Historical Cadastre—0
JOLT-SQL: Joint Loss Tuning of Text-to-SQL with Confusion-aware Noisy Schema SamplingCode0
Cheaper, Better, Faster, Stronger: Robust Text-to-SQL without Chain-of-Thought or Fine-Tuning—0
SQLForge: Synthesizing Reliable and Diverse Data to Enhance Text-to-SQL Reasoning in LLMs—0
CSC-SQL: Corrective Self-Consistency in Text-to-SQL via Reinforcement LearningCode2
ReEx-SQL: Reasoning with Execution-Aware Reinforcement Learning for Text-to-SQL—0
Graph-Reward-SQL: Execution-Free Reinforcement Learning for Text-to-SQL via Graph Matching and Stepwise RewardCode3
Ambiguity Resolution in Text-to-Structured Data Mapping—0
TARGET: Benchmarking Table Retrieval for Generative Tasks—0
Reward-SQL: Boosting Text-to-SQL via Stepwise Reasoning and Process-Supervised RewardsCode1
VTS-LLM: Domain-Adaptive LLM Agent for Enhancing Awareness in Vessel Traffic Services through Natural LanguageCode0
When Reasoning Beats Scale: A 1.5B Reasoning Model Outranks 13B LLMs as DiscriminatorCode0
Fact-Consistency Evaluation of Text-to-SQL Generation for Business Intelligence Using Exaone 3.5—0
Sparks of Tabular Reasoning via Text2SQL Reinforcement Learning—0
Syntactic and Semantic Control of Large Language Models via Sequential Monte Carlo—0
Task-Circuit Quantization: Leveraging Knowledge Localization and Interpretability for CompressionCode1
ELT-Bench: An End-to-End Benchmark for Evaluating AI Agents on ELT PipelinesCode1
Sigma: A dataset for text-to-code semantic parsing with statistical analysisCode0
Query and Conquer: Execution-Guided SQL Generation—0
Reasoning-SQL: Reinforcement Learning with SQL Tailored Partial Rewards for Reasoning-Enhanced Text-to-SQL—0
EllieSQL: Cost-Efficient Text-to-SQL with Complexity-Aware Routing—0
GenEdit: Compounding Operators and Continuous Improvement to Tackle Text-to-SQL in the Enterprise—0
ExCoT: Optimizing Reasoning for Text-to-SQL with Execution FeedbackCode3
LinkAlign: Scalable Schema Linking for Real-World Large-Scale Multi-Database Text-to-SQLCode2
TinySQL: A Progressive Text-to-SQL Dataset for Mechanistic Interpretability Research—0
Valid Text-to-SQL Generation with Unification-based DeepStochLogCode0
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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