Papers Text-To-SQL
“Text-To-SQL” 태그가 달린 논문 424편 · 필터 해제
AnDB: Breaking Boundaries with an AI-Native Database for Universal Semantic Analysis
In this demonstration, we present AnDB, an AI-native database that supports traditional OLTP workloads and innovative AI-driven tasks, enabling unified semantic analysis across structured and unstructured data. While str…
Semantic RetrievalText to SQLText-To-SQLSTaR-SQL: Self-Taught Reasoner for Text-to-SQL
Generating step-by-step "chain-of-thought" rationales has proven effective for improving the performance of large language models on complex reasoning tasks. However, applying such techniques to structured tasks, such as…
Text to SQLText-To-SQLOpenSearch-SQL: Enhancing Text-to-SQL with Dynamic Few-shot and Consistency Alignment
Although multi-agent collaborative Large Language Models (LLMs) have achieved significant breakthroughs in the Text-to-SQL task, their performance is still constrained by various factors. These factors include the incomp…
HallucinationInstruction FollowingModel SelectionText to SQL+1Graph Neural Networks for Databases: A Survey
Graph neural networks (GNNs) are powerful deep learning models for graph-structured data, demonstrating remarkable success across diverse domains. Recently, the database (DB) community has increasingly recognized the pot…
Graph SimilaritySurveyText to SQLText-To-SQLKnapsack Optimization-based Schema Linking for LLM-based Text-to-SQL Generation
Generating SQLs from user queries is a long-standing challenge, where the accuracy of initial schema linking significantly impacts subsequent SQL generation performance. However, current schema linking models still strug…
Text to SQLText-To-SQLSQL-o1: A Self-Reward Heuristic Dynamic Search Method for Text-to-SQL
Text-to-SQL (Text2SQL) aims to map natural language questions to executable SQL queries. Although large language models (LLMs) have driven significant progress, current approaches struggle with poor transferability to op…
Few-Shot LearningHeuristic SearchNatural Language QueriesText to SQL+1Uncovering the Impact of Chain-of-Thought Reasoning for Direct Preference Optimization: Lessons from Text-to-SQL
Direct Preference Optimization (DPO) has proven effective in complex reasoning tasks like math word problems and code generation. However, when applied to Text-to-SQL datasets, it often fails to improve performance and c…
Code GenerationMathText to SQLText-To-SQLSAFE-SQL: Self-Augmented In-Context Learning with Fine-grained Example Selection for Text-to-SQL
Text-to-SQL aims to convert natural language questions into executable SQL queries. While previous approaches, such as skeleton-masked selection, have demonstrated strong performance by retrieving similar training exampl…
In-Context LearningText to SQLText-To-SQLBASE-SQL: A powerful open source Text-To-SQL baseline approach
The conversion of natural language into SQL language for querying databases (Text-to-SQL) has broad application prospects and has attracted widespread attention. At present, the mainstream Text-to-SQL methods are mainly …
In-Context LearningLarge Language ModelText to SQLText-To-SQLCognify: Supercharging Gen-AI Workflows With Hierarchical Autotuning
Today's gen-AI workflows that involve multiple ML model calls, tool/API calls, data retrieval, or generic code execution are often tuned manually in an ad-hoc way that is both time-consuming and error-prone. In this pape…
RAGText to SQLText-To-SQLOpenGrok: Enhancing SNS Data Processing with Distilled Knowledge and Mask-like Mechanisms
This report details Lumen Labs' novel approach to processing Social Networking Service (SNS) data. We leverage knowledge distillation, specifically a simple distillation method inspired by DeepSeek-R1's CoT acquisition, …
Knowledge DistillationMMLUText-To-SQLRationalization Models for Text-to-SQL
We introduce a framework for generating Chain-of-Thought (CoT) rationales to enhance text-to-SQL model fine-tuning. These rationales consist of intermediate SQL statements and explanations, serving as incremental steps t…
Knowledge DistillationLanguage ModelingLanguage ModellingLarge Language Model+2PSM-SQL: Progressive Schema Learning with Multi-granularity Semantics for Text-to-SQL
It is challenging to convert natural language (NL) questions into executable structured query language (SQL) queries for text-to-SQL tasks due to the vast number of database schemas with redundancy, which interferes with…
Text to SQLText-To-SQLTripletReFoRCE: A Text-to-SQL Agent with Self-Refinement, Format Restriction, and Column Exploration
Text-to-SQL systems have unlocked easier access to critical data insights by enabling natural language queries over structured databases. However, deploying such systems in enterprise environments remains challenging due…
Instruction FollowingNatural Language QueriesText to SQLText-To-SQLCoddLLM: Empowering Large Language Models for Data Analytics
Large Language Models (LLMs) have the potential to revolutionize data analytics by simplifying tasks such as data discovery and SQL query synthesis through natural language interactions. This work serves as a pivotal fir…
Multiple-choiceSynthetic Data GenerationText to SQLText-To-SQLMCTS-SQL: An Effective Framework for Text-to-SQL with Monte Carlo Tree Search
Text-to-SQL is a fundamental and longstanding problem in the NLP area, aiming at converting natural language queries into SQL, enabling non-expert users to operate databases. Recent advances in LLM have greatly improved …
Natural Language QueriesText to SQLText-To-SQLBalancing Content Size in RAG-Text2SQL System
Large Language Models (LLMs) have emerged as a promising solution for converting natural language queries into SQL commands, enabling seamless database interaction. However, these Text-to-SQL (Text2SQL) systems face inhe…
Natural Language QueriesRAGRetrievalRetrieval-augmented Generation+2Text-to-SQL based on Large Language Models and Database Keyword Search
Text-to-SQL prompt strategies based on Large Language Models (LLMs) achieve remarkable performance on well-known benchmarks. However, when applied to real-world databases, their performance is significantly less than for…
Text to SQLText-To-SQLExtractive Schema Linking for Text-to-SQL
Text-to-SQL is emerging as a practical interface for real world databases. The dominant paradigm for Text-to-SQL is cross-database or schema-independent, supporting application schemas unseen during training. The schema …
Text to SQLText-To-SQLDialect2SQL: A Novel Text-to-SQL Dataset for Arabic Dialects with a Focus on Moroccan Darija
The task of converting natural language questions (NLQs) into executable SQL queries, known as text-to-SQL, has gained significant interest in recent years, as it enables non-technical users to interact with relational d…
Text to SQLText-To-SQL