Papers Text-To-SQL
“Text-To-SQL” 태그가 달린 논문 424편 · 필터 해제
UNJOIN: Enhancing Multi-Table Text-to-SQL Generation via Schema Simplification
Recent advances in large language models (LLMs) have greatly improved Text-to-SQL performance for single-table queries. But, it remains challenging in multi-table databases due to complex schema and relational operations…
RetrievalText to SQLText-To-SQLBiomedSQL: Text-to-SQL for Scientific Reasoning on Biomedical Knowledge Bases
Biomedical researchers increasingly rely on large-scale structured databases for complex analytical tasks. However, current text-to-SQL systems often struggle to map qualitative scientific questions into executable SQL, …
Causal Inferencescientific discoveryText to SQLText-To-SQLExeSQL: Self-Taught Text-to-SQL Models with Execution-Driven Bootstrapping for SQL Dialects
Recent text-to-SQL models have achieved strong performance, but their effectiveness remains largely confined to SQLite due to dataset limitations. However, real-world applications require SQL generation across multiple d…
Text to SQLText-To-SQLLLM-Powered Agents for Navigating Venice's Historical Cadastre
Cadastral data reveal key information about the historical organization of cities but are often non-standardized due to diverse formats and human annotations, complicating large-scale analysis. We explore as a case study…
HallucinationNatural Language QueriesText to SQLText-To-SQLArctic-Text2SQL-R1: Simple Rewards, Strong Reasoning in Text-to-SQL
Translating natural language into SQL (Test2SQL) is a longstanding challenge at the intersection of natural language understanding and structured data access. While large language models (LLMs) have significantly improve…
Natural Language UnderstandingReinforcement Learning (RL)Text to SQLText-To-SQLCheaper, Better, Faster, Stronger: Robust Text-to-SQL without Chain-of-Thought or Fine-Tuning
LLMs are effective at code generation tasks like text-to-SQL, but is it worth the cost? Many state-of-the-art approaches use non-task-specific LLM techniques including Chain-of-Thought (CoT), self-consistency, and fine-t…
Code GenerationText to SQLText-To-SQLJOLT-SQL: Joint Loss Tuning of Text-to-SQL with Confusion-aware Noisy Schema Sampling
Text-to-SQL, which maps natural language to SQL queries, has benefited greatly from recent advances in Large Language Models (LLMs). While LLMs offer various paradigms for this task, including prompting and supervised fi…
Text to SQLText-To-SQLCSC-SQL: Corrective Self-Consistency in Text-to-SQL via Reinforcement Learning
Large language models (LLMs) have demonstrated strong capabilities in translating natural language questions about relational databases into SQL queries. In particular, test-time scaling techniques such as Self-Consisten…
Text to SQLText-To-SQLReEx-SQL: Reasoning with Execution-Aware Reinforcement Learning for Text-to-SQL
In Text-to-SQL, execution feedback is essential for guiding large language models (LLMs) to reason accurately and generate reliable SQL queries. However, existing methods treat execution feedback solely as a post-hoc sig…
Text to SQLText-To-SQLSQLForge: Synthesizing Reliable and Diverse Data to Enhance Text-to-SQL Reasoning in LLMs
Large Language models (LLMs) have demonstrated significant potential in text-to-SQL reasoning tasks, yet a substantial performance gap persists between existing open-source models and their closed-source counterparts. In…
DiversityText to SQLText-To-SQLGraph-Reward-SQL: Execution-Free Reinforcement Learning for Text-to-SQL via Graph Matching and Stepwise Reward
Reinforcement learning (RL) has been widely adopted to enhance the performance of large language models (LLMs) on Text-to-SQL tasks. However, existing methods often rely on execution-based or LLM-based Bradley-Terry rewa…
GPUGraph MatchingReinforcement Learning (RL)Text to SQL+1Ambiguity Resolution in Text-to-Structured Data Mapping
Ambiguity in natural language is a significant obstacle for achieving accurate text to structured data mapping through large language models (LLMs), which affects the performance of tasks such as mapping text to agentic …
Text to SQLText-To-SQLTARGET: Benchmarking Table Retrieval for Generative Tasks
The data landscape is rich with structured data, often of high value to organizations, driving important applications in data analysis and machine learning. Recent progress in representation learning and generative model…
BenchmarkingRepresentation LearningRetrievalRetrieval-augmented Generation+3Reward-SQL: Boosting Text-to-SQL via Stepwise Reasoning and Process-Supervised Rewards
Recent advances in large language models (LLMs) have significantly improved performance on the Text-to-SQL task by leveraging their powerful reasoning capabilities. To enhance accuracy during the reasoning process, exter…
Text to SQLText-To-SQLVTS-LLM: Domain-Adaptive LLM Agent for Enhancing Awareness in Vessel Traffic Services through Natural Language
Vessel Traffic Services (VTS) are essential for maritime safety and regulatory compliance through real-time traffic management. However, with increasing traffic complexity and the prevalence of heterogeneous, multimodal …
ManagementNatural Language QueriesNERRelational Reasoning+2Fact-Consistency Evaluation of Text-to-SQL Generation for Business Intelligence Using Exaone 3.5
Large Language Models (LLMs) have shown promise in enabling natural language interfaces for structured data querying through text-to-SQL generation. However, their application in real-world Business Intelligence (BI) con…
Arithmetic ReasoningText to SQLText-To-SQLWhen Reasoning Beats Scale: A 1.5B Reasoning Model Outranks 13B LLMs as Discriminator
Large Language Models (LLM) with reasoning capabilities offer a promising path for improving candidate evaluation in planning frameworks, but their relative performance against traditional non-reasoning models remains la…
Text to SQLText-To-SQLSparks of Tabular Reasoning via Text2SQL Reinforcement Learning
This work reframes the Text-to-SQL task as a pathway for teaching large language models (LLMs) to reason over and manipulate tabular data--moving beyond the traditional focus on query generation. We propose a two-stage f…
reinforcement-learningReinforcement LearningText to SQLText-To-SQLSyntactic and Semantic Control of Large Language Models via Sequential Monte Carlo
A wide range of LM applications require generating text that conforms to syntactic or semantic constraints. Imposing such constraints can be naturally framed as probabilistic conditioning, but exact generation from the r…
Code GenerationProbabilistic ProgrammingText to SQLText-To-SQLTask-Circuit Quantization: Leveraging Knowledge Localization and Interpretability for Compression
Post-training quantization (PTQ) reduces a model's memory footprint by mapping full precision weights into low bit weights without costly retraining, but can degrade its downstream performance especially in low 2- to 3-b…
MathMMLUQuantizationText to SQL+1