arXiv AI June 9, 2026 By Jianling Gao, Chongyang Tao, Jiayuan Bai, Liu Yang, Xuanguang Pan, Jinrui Liu, Shihao Xing, Xiaohan Xu, Jie Liang, Shuai Ma

UniQL: Towards Dialect-Universal Benchmarking for Text-to-SQL

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arXiv:2606. 08018v1 Announce Type: new Abstract: Existing text-to-SQL benchmarks are largely centered on SQLite, making it difficult to evaluate whether models can generalize across heterogeneous SQL dialects.

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arXiv Machine Learning

Sep 22

Dial: A Knowledge-Grounded Dialect-Specific NL2SQL System

The paper introduces Dial, a knowledge‑grounded framework for generating SQL queries that respect the specific dialect of heterogeneous database systems. Dial comprises a Dialect‑Aware Logical Query Planning module, a hierarchical intent‑aware knowledge base (HINT‑KB) containing canonical syntax, function, and constraint repositories, and an execution‑driven debugging loop that separates syntactic recovery from logic auditing. The authors also present DS‑NL2SQL, a benchmark of 2,218 dialect‑specific test cases across six major database systems, and report that Dial improves translation accuracy by 10.25% and dialect feature coverage by 15.77% over existing baselines.

By Xiang Zhang, Hongming Xu, Le Zhou, Wei Zhou, Xuanhe Zhou, Guoliang Li, Yuyu Luo, Changdong Liu, Guorun Chen, Jiang Liao, Fan Wu

rag fine-tuning 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

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

Sep 1

BIRD-History: A Benchmark for History-Driven Text-to-SQL with Fine-Grained Knowledge Annotations

arXiv:2608.29345v1 Announce Type: new Abstract: While recent Large Language Model (LLM)-based text-to-SQL systems achieve impressive performance on standard benchmarks, they struggle when user querie...

By Yunfan Zhou, Qiming Shi, Yizhou Yang, Di Weng, Yingcai Wu

llms 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 Computation and Language

Sep 22

An Iterative LangGraph Agent for Text-to-SQL: Natural Language Access to the Chicago Crime Database

arXiv:2609.22917v1 Announce Type: new Abstract: Non-technical stakeholders frequently cannot write the SQL needed to extract insights from operational databases. We built and evaluated a Text-to-SQL...

By Vigneshwar Ravi Rao, Rupesh Swarnakar, Fayeq Jeelani Syed{\dag}

llms agents fine-tuning benchmarks data-engineering

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Dial: A Knowledge-Grounded Dialect-Specific NL2SQL System

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