arXiv AI

Progress-SQL: Improving Reinforcement Learning for Text-to-SQL via Progressive Rewards

arXiv:2606. 06825v1 Announce Type: cross Abstract: Reinforcement learning has recently shown promise in improving large language models for Text-to-SQL generation, yet existing methods typically optimize one-shot rewards defined over a single SQL state.

arXiv Machine Learning

Jun 15

Reward-SQL: Boosting Text-to-SQL via Stepwise Execution-Aware Reasoning and Process-Supervised Rewards

arXiv:2505. 04671v3 Announce Type: replace-cross Abstract: Recent advances in large language models (LLMs) trained with reinforcement learning (RL) have improved Text-to-SQL performance.

By Yuxin Zhang, Meihao Fan, Ju Fan, Mingyang Yi, Yuyu Luo, Guoliang Li, Bin Wu, Wenchao Zhou

llms reinforcement-learning safety data-engineering

arXiv AI

Jul 1

Test-Time Verification for Text-to-SQL via Outcome Reward Models

arXiv:2606. 30851v1 Announce Type: cross Abstract: Improving the reliability of large language models (LLMs) at inference time is a central challenge in structured reasoning tasks such as Text-to-SQL.

By Mattia Tritto, Giuseppe Farano, Dario Di Palma, Gaetano Rossiello, Fedelucio Narducci, Dharmashankar Subramanian, Tommaso Di Noia

llms benchmarks safety data-engineering

arXiv AI

1d ago

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

Jul 24

EvoSQL: Memory-Augmented Critic-Generator Co-Evolution for Text-to-SQL

arXiv:2607. 20489v1 Announce Type: new Abstract: Text-to-SQL has advanced rapidly with large language models, but complex database queries still require reasoning beyond one-shot generation, including multi-step decomposition, execution-based diagnosis, and targeted correction.

By Jiawei Zhou, Jianwei Wang, Chenyu Zhou, Chaojian Shi, Ming Dong, Kai Wang

llms fine-tuning efficiency data-engineering

arXiv AI

Jul 17

QDA-SQL: Questions Enhanced Dialogue Augmentation for Multi-Turn Text-to-SQL

arXiv:2406. 10593v3 Announce Type: replace Abstract: Fine-tuning large language models (LLMs) for specific domain tasks has achieved great success in Text-to-SQL tasks.

By Yinggang Sun, Ziming Guo, Haining Yu, Chuanyi Liu, Xiang Li, Bingxuan Wang, Xiangzhan Yu, Tiancheng Zhao

llms fine-tuning data-engineering

arXiv Machine Learning

Aug 6

RingSQL: Schema-Independent Synthetic Data Generation for Text-to-SQL Reinforcement Learning

arXiv:2601. 05451v2 Announce Type: replace Abstract: Recent advances in text-to-SQL have been driven by larger models, better datasets, and new training methods like RLVR.

By Marko Sterbentz, Kevin Cushing, Cameron Barrie, Kristian J. Hammond

llms reinforcement-learning benchmarks data-engineering

arXiv AI

Jun 16

Understanding, Detecting, and Repairing Real-World In-Context-Learning-Based Text-to-SQL Errors

arXiv:2501. 09310v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have been adopted for text-to-SQL tasks, utilizing their in-context learning (ICL) capability to translate natural language questions into SQL queries.

By Jiawei Shen, Chengcheng Wan, Ruoyi Qiao, Jiazhen Zou, Hang Xu, Yuchen Shao, Yueling Zhang, Weikai Miao, Geguang Pu

llms benchmarks data-engineering

arXiv AI

Jun 17

DecoSearch: Complexity-Aware Routing and Plan-Level Repair for Text-to-SQL

arXiv:2606. 17821v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in translating natural language to SQL, yet existing methods still falter on complex queries requiring multi-step, data-aware reasoning.

By Esteban Schafir, Xu Zheng, Hojat Allah Salehi, Zhuomin Chen, Mo Sha, Wei Cheng, Dongsheng Luo

llms rag fine-tuning data-engineering

arXiv AI

6d ago

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

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

By Anik Pramanik, Murat Kantarcioglu, Vincent Oria, Shantanu Sharma

llms fine-tuning benchmarks safety 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

arXiv AI

Jun 2

A Unified Evaluation-Instructed Framework for Query-Dependent Prompt Optimization

arXiv:2511. 19829v2 Announce Type: replace Abstract: Most prompt-optimization methods refine a single static template, making them ineffective in complex and dynamic user scenarios.

By Ke Chen, Yifeng Wang, Hassan Almosapeeh, Haohan Wang

arXiv AI

Aug 11

SafeQL: Search-based Refinement for Safe and Efficient LLM-based Text-to-SQL

arXiv:2608. 09260v1 Announce Type: cross Abstract: Large language models (LLMs) have advanced Text-to-SQL by enabling natural language interfaces to databases without task-specific fine-tuning.

By Geonho Lee, Min-Soo Kim

llms fine-tuning benchmarks data-engineering

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