Papers with Code 아카이브
Text-To-SQL
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 10
- 보존된 평가 기록
- 86
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.
<span style="color:grey; opacity: 0.6">( Image credit: SyntaxSQLNet )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
CogniSQL-R1-Zero: Lightweight Reinforced Reasoning for Efficient SQL Generation
XiYan-SQL: A Novel Multi-Generator Framework For Text-to-SQL
SWE-SQL: Illuminating LLM Pathways to Solve User SQL Issues in Real-World Applications
Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task
Bridging the Gap Between Open-Source and Proprietary LLMs in Table QA
LLM-Driven Data Generation and a Novel Soft Metric for Evaluating Text-to-SQL in Aviation MRO
HI-SQL: Optimizing Text-to-SQL Systems through Dynamic Hint Integration
SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation
SDE-SQL: Enhancing Text-to-SQL Generation in Large Language Models via Self-Driven Exploration with SQL Probes
SQLens: An End-to-End Framework for Error Detection and Correction in Text-to-SQL
Reasoning-Table: Exploring Reinforcement Learning for Table Reasoning
Pi-SQL: Enhancing Text-to-SQL with Fine-Grained Guidance from Pivot Programming Languages
SHARE: An SLM-based Hierarchical Action CorREction Assistant for Text-to-SQL
Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunities
Calibrating LLMs for Text-to-SQL Parsing by Leveraging Sub-clause Frequencies
DCG-SQL: Enhancing In-Context Learning for Text-to-SQL with Deep Contextual Schema Link Graph
Meta-aware Learning in text-to-SQL Large Language Model
Structuring the Unstructured: A Multi-Agent System for Extracting and Querying Financial KPIs and Guidance
LogicCat: A Chain-of-Thought Text-to-SQL Benchmark for Multi-Domain Reasoning Challenges
UNJOIN: Enhancing Multi-Table Text-to-SQL Generation via Schema Simplification
SchemaGraphSQL: Efficient Schema Linking with Pathfinding Graph Algorithms for Text-to-SQL on Large-Scale Databases
BiomedSQL: Text-to-SQL for Scientific Reasoning on Biomedical Knowledge Bases
LLM-Powered Agents for Navigating Venice's Historical Cadastre
ExeSQL: Self-Taught Text-to-SQL Models with Execution-Driven Bootstrapping for SQL Dialects
Arctic-Text2SQL-R1: Simple Rewards, Strong Reasoning in Text-to-SQL
Cheaper, Better, Faster, Stronger: Robust Text-to-SQL without Chain-of-Thought or Fine-Tuning
JOLT-SQL: Joint Loss Tuning of Text-to-SQL with Confusion-aware Noisy Schema Sampling
ReEx-SQL: Reasoning with Execution-Aware Reinforcement Learning for Text-to-SQL
CSC-SQL: Corrective Self-Consistency in Text-to-SQL via Reinforcement Learning
SQLForge: Synthesizing Reliable and Diverse Data to Enhance Text-to-SQL Reasoning in LLMs
Graph-Reward-SQL: Execution-Free Reinforcement Learning for Text-to-SQL via Graph Matching and Stepwise Reward
Ambiguity Resolution in Text-to-Structured Data Mapping
TARGET: Benchmarking Table Retrieval for Generative Tasks
Reward-SQL: Boosting Text-to-SQL via Stepwise Reasoning and Process-Supervised Rewards
VTS-LLM: Domain-Adaptive LLM Agent for Enhancing Awareness in Vessel Traffic Services through Natural Language
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.