arXiv Machine Learning July 27, 2026 By Denis Mayr Lima Martins, Gottfried Vossen

Prompt as a Data Type: In-Database LLM Prompt Management and Rewriting

Read the original on arXiv Machine Learning →

arXiv:2607. 21756v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used in database-backed applications to classify tuples, filter records using semantic predicates, extract structured attributes, and enrich query results.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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 9

Knowing How to Edit: Reliable Evaluation Signals for Diagnosing and Optimizing Prompts at Query Level

arXiv:2511. 19829v3 Announce Type: replace Abstract: Prompt optimization has become a central mechanism for eliciting strong performance from LLMs, and recent work has made substantial progress by proposing diverse prompt evaluation metrics and optimization strategies.

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

llms benchmarks

arXiv AI

Jul 7

TabQueryBench: A Query-Centric Benchmark for Synthetic Tabular Data

arXiv:2607. 03926v1 Announce Type: cross Abstract: Synthetic tabular data support use cases like data sharing, model development under access restrictions, and rapid prototyping of analytical workflows.

By Jialin Zhang, Fenghao Dong, Yajie Zhou, Vyas Sekar, Shinan Liu

benchmarks safety data-engineering

arXiv AI

Jun 17

Querying an astronomical database using large language models: the ALeRCE text-to-SQL system

arXiv:2606. 18108v1 Announce Type: cross Abstract: We develop a text-to-SQL (structured query language) system based on large language models (LLMs) using in-context learning and apply it to the Automatic Learning for the Rapid Classification of Events (ALeRCE) astronomical database.

By P. A. Estevez, J. Espejo-Moreira, S. Sanfeliu-Alvarez, F. Forster, A. M. Munoz Arancibia, G. Cabrera-Vives, F. E. Bauer, A. Bayo, M. Catelan, R. Dastidar, L. Hernandez-Garcia, J. A. Intriago, G. Pignata

llms 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

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

Related stories

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

Knowing How to Edit: Reliable Evaluation Signals for Diagnosing and Optimizing Prompts at Query Level

TabQueryBench: A Query-Centric Benchmark for Synthetic Tabular Data

Querying an astronomical database using large language models: the ALeRCE text-to-SQL system

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

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