Papers Text-To-SQL
“Text-To-SQL” 태그가 달린 논문 424편 · 필터 해제
ELT-Bench: An End-to-End Benchmark for Evaluating AI Agents on ELT Pipelines
Practitioners are increasingly turning to Extract-Load-Transform (ELT) pipelines with the widespread adoption of cloud data warehouses. However, designing these pipelines often involves significant manual work to ensure …
AI AgentText to SQLText-To-SQLSigma: A dataset for text-to-code semantic parsing with statistical analysis
In the domain of semantic parsing, significant progress has been achieved in Text-to-SQL and question-answering tasks, both of which focus on extracting information from data sources in their native formats. However, the…
Question AnsweringSemantic ParsingText to SQLText-To-SQLQuery and Conquer: Execution-Guided SQL Generation
We propose a novel approach for generating complex outputs that significantly improves accuracy in text-to-SQL tasks. Our method leverages execution results to select the most semantically consistent query from multiple …
Text to SQLText-To-SQLReasoning-SQL: Reinforcement Learning with SQL Tailored Partial Rewards for Reasoning-Enhanced Text-to-SQL
Text-to-SQL is a challenging task involving multiple reasoning-intensive subtasks, including natural language understanding, database schema comprehension, and precise SQL query formulation. Existing approaches often rel…
Natural Language UnderstandingReinforcement Learning (RL)Text to SQLText-To-SQLEllieSQL: Cost-Efficient Text-to-SQL with Complexity-Aware Routing
Text-to-SQL automatically translates natural language queries to SQL, allowing non-technical users to retrieve data from databases without specialized SQL knowledge. Despite the success of advanced LLM-based Text-to-SQL …
Natural Language QueriesText to SQLText-To-SQLGenEdit: Compounding Operators and Continuous Improvement to Tackle Text-to-SQL in the Enterprise
Recent advancements in Text-to-SQL, driven by large language models, are democratizing data access. Despite these advancements, enterprise deployments remain challenging due to the need to capture business-specific knowl…
RetrievalText to SQLText-To-SQLExCoT: Optimizing Reasoning for Text-to-SQL with Execution Feedback
Text-to-SQL demands precise reasoning to convert natural language questions into structured queries. While large language models (LLMs) excel in many reasoning tasks, their ability to leverage Chain-of-Thought (CoT) reas…
Text to SQLText-To-SQLLinkAlign: Scalable Schema Linking for Real-World Large-Scale Multi-Database Text-to-SQL
Schema linking is a critical bottleneck in achieving human-level performance in Text-to-SQL tasks, particularly in real-world large-scale multi-database scenarios. Addressing schema linking faces two major challenges: (1…
RetrievalText to SQLText-To-SQLValid Text-to-SQL Generation with Unification-based DeepStochLog
Large language models have been used to translate natural language questions to SQL queries. Without hard constraints on syntax and database schema, they occasionally produce invalid queries that are not executable. Thes…
Language ModelingLanguage ModellingNatural Language UnderstandingText to SQL+2TinySQL: A Progressive Text-to-SQL Dataset for Mechanistic Interpretability Research
Mechanistic interpretability research faces a gap between analyzing simple circuits in toy tasks and discovering features in large models. To bridge this gap, we propose text-to-SQL generation as an ideal task to study, …
Intent RecognitionText to SQLText-To-SQLGenicious: Contextual Few-shot Prompting for Insights Discovery
Data and insights discovery is critical for decision-making in modern organizations. We present Genicious, an LLM-aided interface that enables users to interact with tabular datasets and ask complex queries in natural la…
BenchmarkingDecision MakingText to SQLText-To-SQLDatrics Text2SQL. A Framework for Natural Language to SQL Query Generation
Datrics Text2SQL is a Text-to-SQL framework using Retrieval-Augmented Generation (RAG) to enhance accuracy and reliability. By integrating domain knowledge, database structure, and example-based learning, it addresses co…
Natural Language QueriesRAGRetrievalRetrieval-augmented Generation+2SQLCritic: Correcting Text-to-SQL Generation via Clause-wise Critic
Existing refinement methods in LLM-based Text-to-SQL systems exhibit limited effectiveness. They often introduce new errors during the self-correction process and fail to detect and correct semantic inaccuracies. To addr…
Natural Language QueriesText to SQLText-To-SQLDB-Explore: Automated Database Exploration and Instruction Synthesis for Text-to-SQL
Recent text-to-SQL systems powered by large language models (LLMs) have demonstrated remarkable performance in translating natural language queries into SQL. However, these systems often struggle with complex database st…
Logical ReasoningNatural Language QueriesText to SQLText-To-SQLOmniSQL: Synthesizing High-quality Text-to-SQL Data at Scale
Text-to-SQL, the task of translating natural language questions into SQL queries, plays a crucial role in enabling non-experts to interact with databases. While recent advancements in large language models (LLMs) have si…
Text to SQLText-To-SQLAutomatic database description generation for Text-to-SQL
In the context of the Text-to-SQL task, table and column descriptions are crucial for bridging the gap between natural language and database schema. This report proposes a method for automatically generating effective da…
Text to SQLText-To-SQLDisambiguate First Parse Later: Generating Interpretations for Ambiguity Resolution in Semantic Parsing
Handling ambiguity and underspecification is an important challenge in natural language interfaces, particularly for tasks like text-to-SQL semantic parsing. We propose a modular approach that resolves ambiguity using na…
Semantic ParsingText to SQLText-To-SQLLEDD: Large Language Model-Empowered Data Discovery in Data Lakes
Data discovery in data lakes with ever increasing datasets has long been recognized as a big challenge in the realm of data management, especially for semantic search of and hierarchical global catalog generation of tabl…
Language ModelingLanguage ModellingLarge Language ModelManagement+3Text-to-SQL Domain Adaptation via Human-LLM Collaborative Data Annotation
Text-to-SQL models, which parse natural language (NL) questions to executable SQL queries, are increasingly adopted in real-world applications. However, deploying such models in the real world often requires adapting the…
Domain AdaptationText to SQLText-To-SQLBridging the Gap: Transforming Natural Language Questions into SQL Queries via Abstract Query Pattern and Contextual Schema Markup
Large language models have demonstrated excellent performance in many tasks, including Text-to-SQL, due to their powerful in-context learning capabilities. They are becoming the mainstream approach for Text-to-SQL. Howev…
In-Context LearningText to SQLText-To-SQL