arXiv AI

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

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

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

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

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

Aug 18

Bounded Semantic Planning and Deterministic Compilation for Reliable Enterprise Text-to-SQL

arXiv:2608. 16663v1 Announce Type: cross Abstract: Direct text-to-SQL asks a language model to do two jobs: interpret the business question and construct the complete relational query.

By Yi Ai

llms benchmarks data-engineering

arXiv AI

Jul 24

ReliableTableQA:How Much Supervision Does Reliability Annotation Need?

arXiv:2607. 20537v1 Announce Type: cross Abstract: We introduce ReliableTableQA, a framework for training an LLM to annotate the statistical reliability of tabular QA results, not whether the query is answerable, but whether the computed answer is statistically meaningful.

By Huei-Chung Hu, Hsin-Tai Wu, Koyo Kobayashi

llms fine-tuning 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

Hugging Face Trending Papers

Jul 14

Finding the Right Tables and Columns: A Benchmark and Corpus-Adaptive Embeddings for SQL Schema Retrieval

Retrieval in the SQL setting has largely been studied as the task of finding, within a large collection of SQL statements, the statement that answers a natural-language question. At scale, however, a more fundamental retrieval problem precedes generation: schema retrieval, identifying the tables and columns a question requires in a database that may contain thousands of them, far more than fit in a model's context.

rag fine-tuning benchmarks data-engineering

Related stories