Papers Text-To-SQL
“Text-To-SQL” 태그가 달린 논문 424편 · 필터 해제
CogniSQL-R1-Zero: Lightweight Reinforced Reasoning for Efficient SQL Generation
Translating natural language into SQL (Text-to-SQL) remains a core challenge at the intersection of language understanding and structured data access. Although large language models (LLMs) have improved fluency, generati…
Reinforcement Learning (RL)TAGText to SQLText-To-SQLXiYan-SQL: A Novel Multi-Generator Framework For Text-to-SQL
To leverage the advantages of LLM in addressing challenges in the Text-to-SQL task, we present XiYan-SQL, an innovative framework effectively generating and utilizing multiple SQL candidates. It consists of three compone…
Text to SQLText-To-SQLSWE-SQL: Illuminating LLM Pathways to Solve User SQL Issues in Real-World Applications
Resolution of complex SQL issues persists as a significant bottleneck in real-world database applications. Current Large Language Models (LLMs), while adept at text-to-SQL translation, have not been rigorously evaluated …
Text to SQLText-To-SQLSchema-R1: A reasoning training approach for schema linking in Text-to-SQL Task
Schema linking is a critical step in Text-to-SQL task, aiming to accurately predict the table names and column names required for the SQL query based on the given question. However, current fine-tuning approaches for sch…
reinforcement-learningReinforcement LearningText to SQLText-To-SQLBridging the Gap Between Open-Source and Proprietary LLMs in Table QA
This paper presents a system developed for SemEval 2025 Task 8: Question Answering (QA) over tabular data. Our approach integrates several key components: text-to-SQL and text-to-code generation modules, a self-correctio…
Code GenerationLanguage ModelingLanguage ModellingLarge Language Model+6LLM-Driven Data Generation and a Novel Soft Metric for Evaluating Text-to-SQL in Aviation MRO
The application of Large Language Models (LLMs) to text-to-SQL tasks promises to democratize data access, particularly in critical industries like aviation Maintenance, Repair, and Operation (MRO). However, progress is h…
Text to SQLText-To-SQLHI-SQL: Optimizing Text-to-SQL Systems through Dynamic Hint Integration
Text-to-SQL generation bridges the gap between natural language and databases, enabling users to query data without requiring SQL expertise. While large language models (LLMs) have significantly advanced the field, chall…
Hint GenerationText to SQLText-To-SQLSEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation
Text-to-SQL enables non-experts to retrieve data from databases by converting natural language queries into SQL. However, state-of-the-art text-to-SQL studies rely on the BIRD dataset, which assumes that evidence is prov…
Natural Language QueriesText to SQLText-To-SQLSDE-SQL: Enhancing Text-to-SQL Generation in Large Language Models via Self-Driven Exploration with SQL Probes
Recent advancements in large language models (LLMs) have significantly improved performance on the Text-to-SQL task. However, prior approaches typically rely on static, pre-processed database information provided at infe…
Text to SQLText-To-SQLSQLens: An End-to-End Framework for Error Detection and Correction in Text-to-SQL
Text-to-SQL systems translate natural language (NL) questions into SQL queries, enabling non-technical users to interact with structured data. While large language models (LLMs) have shown promising results on the text-t…
Text to SQLText-To-SQLvalidReasoning-Table: Exploring Reinforcement Learning for Table Reasoning
Table reasoning, encompassing tasks such as table question answering, fact verification, and text-to-SQL, requires precise understanding of structured tabular data, coupled with numerical computation and code manipulatio…
Fact VerificationLanguage ModelingLanguage ModellingLarge Language Model+6Pi-SQL: Enhancing Text-to-SQL with Fine-Grained Guidance from Pivot Programming Languages
Text-to-SQL transforms the user queries from natural language to executable SQL programs, enabling non-experts to interact with complex databases. Existing prompt-based methods craft meticulous text guidelines and exampl…
Text to SQLText-To-SQLvalidSHARE: An SLM-based Hierarchical Action CorREction Assistant for Text-to-SQL
Current self-correction approaches in text-to-SQL face two critical limitations: 1) Conventional self-correction methods rely on recursive self-calls of LLMs, resulting in multiplicative computational overhead, and 2) LL…
Text to SQLText-To-SQLExploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunities
Converting natural language (NL) questions into SQL queries, referred to as Text-to-SQL, has emerged as a pivotal technology for facilitating access to relational databases, especially for users without SQL knowledge. Re…
Text to SQLText-To-SQLCalibrating LLMs for Text-to-SQL Parsing by Leveraging Sub-clause Frequencies
While large language models (LLMs) achieve strong performance on text-to-SQL parsing, they sometimes exhibit unexpected failures in which they are confidently incorrect. Building trustworthy text-to-SQL systems thus requ…
SQL ParsingText to SQLText-To-SQLDCG-SQL: Enhancing In-Context Learning for Text-to-SQL with Deep Contextual Schema Link Graph
Text-to-SQL, which translates a natural language question into an SQL query, has advanced with in-context learning of Large Language Models (LLMs). However, existing methods show little improvement in performance compare…
In-Context LearningText to SQLText-To-SQLMeta-aware Learning in text-to-SQL Large Language Model
The advancements of Large language models (LLMs) have provided great opportunities to text-to-SQL tasks to overcome the main challenges to understand complex domain information and complex database structures in business…
Language ModelingLanguage ModellingLarge Language ModelText to SQL+1Structuring the Unstructured: A Multi-Agent System for Extracting and Querying Financial KPIs and Guidance
Extracting structured and quantitative insights from unstructured financial filings is essential in investment research, yet remains time-consuming and resource-intensive. Conventional approaches in practice rely heavily…
Natural Language QueriesRetrievalText to SQLText-To-SQLLogicCat: A Chain-of-Thought Text-to-SQL Benchmark for Multi-Domain Reasoning Challenges
Text-to-SQL is a fundamental task in natural language processing that seeks to translate natural language questions into meaningful and executable SQL queries. While existing datasets are extensive and primarily focus on…
BenchmarkingMathematical ReasoningText to SQLText-To-SQLSchemaGraphSQL: Efficient Schema Linking with Pathfinding Graph Algorithms for Text-to-SQL on Large-Scale Databases
Text-to-SQL systems translate natural language questions into executable SQL queries, and recent progress with large language models (LLMs) has driven substantial improvements in this task. Schema linking remains a criti…
Text to SQLText-To-SQL