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 1–10 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
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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
1XiYan-SQLExecution Accuracy69.86—Unverified