arXiv AI

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.

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

Sep 21

The Stochastic Shift: A New Evaluation Paradigm for Text-to-SQL with AI Operators

arXiv:2609.21133v1 Announce Type: cross Abstract: SQL has been augmented with AI operators, enabling modern data analytics platforms to derive insights from both structured and unstructured data. We...

By Tarfah Alrashed, Fatma Ozcan, Per Jacobsson, Tal Neiman, Xianshun Chen

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 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 AI

Jul 8

Spider 2.0-AIFunc: Extending Real-World Text-to-SQL to AI-Native SQL Workflows

arXiv:2607. 06229v1 Announce Type: cross Abstract: Major cloud data platforms now expose large language model capabilities as native SQL functions, enabling analysts to perform classification, filtering, sentiment analysis, extraction, similarity search, and aggregation within ordinary SQL queries.

By Tianyang Liu, Canwen Xu, Fangyu Lei, Nikki Lijing Kuang, Jixuan Chen, Tao Yu, Julian McAuley, Zhewei Yao, Yuxiong He

llms agents nlp benchmarks data-engineering

arXiv AI

Jun 2

APEX-SQL: Talking to the data via Agentic Exploration for Text-to-SQL

arXiv:2602. 16720v2 Announce Type: replace-cross Abstract: Text-to-SQL systems powered by Large Language Models have excelled on academic benchmarks but struggle in complex enterprise environments.

By Bowen Cao, Weibin Liao, Yushi Sun, Dong Fang, Haitao Li, Wai Lam

llms agents efficiency benchmarks data-engineering

arXiv AI

Sep 10

SQLMorph: Query Mutation and Fine-Grained Metrics for Text-to-SQL Evaluation

arXiv:2609.08950v1 Announce Type: cross Abstract: Text-to-SQL systems translate natural language queries into executable SQL, democratizing access to structured data. Despite recent advances driven b...

By Mohammadhossein Malekpour, Mohamed Riahi, Maxime Lamothe, Amine Mhedhbi

llms 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 AI

Jun 8

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.

By Shihao Zhang, Xiaoman Wang, Yuan Liu, Yunshi Lan, Weining Qian

llms reinforcement-learning safety data-engineering

arXiv Computation and Language

4d ago

IESR:Efficient MCTS-Based Modular Reasoning for Text-to-SQL with Large Language Models

arXiv:2602.05385v2 Announce Type: replace Abstract: Text-to-SQL is a key natural language processing task that maps natural language questions to SQL queries, enabling intuitive interaction with web-...

By Tao Liu, Jiafan Lu, Bohan Yu, Pengcheng Wu, Liu Haixin, Guoyu Xu, Li Xiangheng, Lixiao Li, Jiaming Hou, Zhao Shijun, Xinglin Lyu, Kunli Zhang, Yuxiang Jia, Hongyin Zan

llms nlp fine-tuning benchmarks data-engineering

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

arXiv AI

Jun 2

BADGER: Bridging Agentic and Deterministic Evaluation for Generative Enterprise Reasoning

arXiv:2606. 02109v1 Announce Type: new Abstract: Enterprise AI systems that translate natural language into SQL queries and orchestrate multi-step agentic reasoning pipelines require evaluation approaches fundamentally different from academic benchmarks.

By Shannon Serrao, Soumitra Chatterjee, Dorina Strori, Abhishek Sharma, Nathan Miller

llms agents benchmarks data-engineering

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