Reflect‑SQL is a new framework for converting natural language into SQL queries. It tackles challenges such as large, obscure database schemas, poor table and column retrieval, and syntactically or logically flawed SQL by using a multi‑stage self‑reflection approach. The system iteratively refines queries and SQL through feedback loops driven by an LLM‑as‑a‑judge, achieving 72.03% execution accuracy on the BIRD benchmark, outperforming existing baselines.
By Anupreksha Jain, Manish Shrivastava
arXiv:2607. 22622v1 Announce Type: cross Abstract: Recent Text-to-SQL methods rely heavily on reasoning-centric paradigms such as Chain-of-Thought (CoT), achieving substantial gains on complex benchmarks at the cost of high inference-time overhead.
By Soohyuk Jang, Jiheum Yeom, Nohil Park, Sang Hun Kim, Yoonyoung Choi, Kiwook Bae, Sungroh Yoon
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
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
LIMIT (Less Is More for Instruction Tuning in Text-to-SQL) challenges the belief that large instruction corpora are necessary for effective Text-to-SQL models. The framework uses a four‑stage data‑centric process—difficulty‑aware filtering, chain‑of‑thought synthesis, LLM‑as‑judge quality scoring, and genetic algorithm optimization—to select a compact set of examples that still achieve full schema coverage. On the BIRD and Spider benchmarks, LIMIT’s 796 and 863 samples enable Qwen3‑8B to reach 69.1% and 88.9% execution accuracy, outperforming methods trained on twenty times more data and setting a new state‑of‑the‑art for open‑source approaches.
By Haoyuan Ma, Hengwei Liu, Linjuan Wu, Yongliang Shen, Weiming Lu
arXiv:2607. 11207v1 Announce Type: cross Abstract: Table-based reasoning with large language models (LLMs), which requires reasoning based on natural language questions and structured tabular data, has gained widespread attention.
By Pei Guo, Enjie Liu, Yunzhi Tan, Mochi Gao, Jianxin Zhang, Ruichao Zhong, Juntao Li, Bo Hu, Zang Li
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
The paper introduces FlockMTL, an extension for database management systems that deeply integrates large language models and retrieval‑augmented generation into DuckDB. It provides model‑driven scalar and aggregate functions, cost‑based optimizations like batching and caching, and new SQL DDL abstractions (PROMPT and MODEL) to treat LLMs as first‑class schema objects. These features aim to simplify the development of knowledge‑intensive analytical applications by reducing the effort required to orchestrate heterogeneous data systems and manage LLM context.
By Anas Dorbani, Sunny Yasser, Jimmy Lin, Amine Mhedhbi
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
arXiv:2608. 03794v1 Announce Type: cross Abstract: Large Language Models (LLMs) are transforming database interaction paradigms, evolving from simple query translators to autonomous database administrators (DBAs).
By Shunfan Zheng, Dongsheng Shi, Yue Li, Xin Yi, Linlin Wang, Gerard de Melo
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
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