arXiv AI August 13, 2026 By Anik Pramanik, Murat Kantarcioglu, Vincent Oria, Shantanu Sharma

DexterSQL: Deep Schema Exploration and Rule-based Correction for Text-to-SQL Generation

arXiv:2608. 11889v1 Announce Type: cross Abstract: Prompting-based (\textit{i}.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jul 9

What Predicts Correctness in Text-to-SQL? A Selective-Prediction Study

arXiv:2607. 06799v1 Announce Type: cross Abstract: Evaluating uncertainty in AI-generated SQL queries requires estimating whether a query is correct, where correct means it executes to the same result as a human-written reference.

By Robert Richardson

llms fine-tuning benchmarks data-engineering

arXiv AI

Jun 11

TAHOE: Text-to-SQL with Automated Hint Optimization from Experience

arXiv:2606. 12387v1 Announce Type: cross Abstract: Large Language Models (LLMs) have democratized database access through Text-to-SQL, but moving from prototypes to production remains difficult.

By Zhiyi Chen, Jie Song, Peng Li

llms agents fine-tuning data-engineering

arXiv Computation and Language

Aug 24

Human-Level Text-to-SQL via Reinforcement Learning on Verified Data, Without Pipeline Engineering

arXiv:2603.20004v4 Announce Type: replace-cross Abstract: Translating natural language questions to SQL queries (Text-to-SQL) is a long-standing problem in database research. Recent efforts have focu...

By Yuxuan Zhu, Tengjun Jin, Yoojin Choi, Daniel Kang

llms reinforcement-learning fine-tuning benchmarks data-engineering

arXiv Computation and Language

Sep 21

COAL-SQL: Coverage-Guided Augmentation and Failure-Driven Learning for Text-to-SQL Post-Training

arXiv:2609.20842v1 Announce Type: new Abstract: Text-to-SQL translates natural-language questions into executable SQL queries, but open-source large language models still require task-specific post-t...

By Qifeng Cai, Xuanguang Pan, Hao Liang, Chang Xu, Wentao Zhang

llms reinforcement-learning fine-tuning data-engineering

arXiv AI

Aug 18

ACTS-SQL: Agentic and Critic-Oriented Tree-Structured SQL Correctness with Large Language Models

arXiv:2608. 15145v1 Announce Type: new Abstract: Large Language Models (LLMs) have been increasingly adopted in Text-to-SQL systems, yet SQL errors remain a major obstacle in real-world Text-to-SQL inference pipelines.

By Xinmei Huang, Jie Song, Peng Li, Fuxin Jiang, Jing Zhang, Tieying Zhang, Jianjun Chen, Chenming Liu, Tao Yang, Maoyin Liu, Wenda Li, Hong Chen, Cuiping Li

llms agents efficiency benchmarks data-engineering

arXiv AI

Jun 16

Integrating Reasoning and Generalization in Text-to-SQL via Self-Enhanced Fine-Tuning

arXiv:2606. 15598v1 Announce Type: new Abstract: Text-to-SQL aims to translate natural language questions into executable SQL queries over structured databases, enabling non-expert users to access data intuitively.

By Feng Lyu, Jinfeng Cen, Sijing Duan, Hao Wu, Shucheng Li, Weixu Zhang, Haolun Wu

llms fine-tuning benchmarks data-engineering

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What Predicts Correctness in Text-to-SQL? A Selective-Prediction Study

TAHOE: Text-to-SQL with Automated Hint Optimization from Experience

Human-Level Text-to-SQL via Reinforcement Learning on Verified Data, Without Pipeline Engineering

COAL-SQL: Coverage-Guided Augmentation and Failure-Driven Learning for Text-to-SQL Post-Training

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Integrating Reasoning and Generalization in Text-to-SQL via Self-Enhanced Fine-Tuning