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: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
text2ql is an open‑source Python framework that enables natural language querying of databases without relying on large language models at query time. It uses a language‑agnostic intermediate representation (QueryIR) and a pluggable renderer to support both SQL and GraphQL targets through a single seven‑stage detection pipeline. In deterministic mode, it achieves 100% execution accuracy with a median latency of 3.2 ms, while the LLM‑backed mode delivers 62‑70% exact match and 84‑91% execution accuracy on benchmark samples.
By Ritesh Kumar
text2ql is an open‑source Python framework that enables natural language querying of databases without being limited to SQL, without requiring large language model inference at query time, and with a runtime confidence score for each generated query. It uses a language‑agnostic Intermediate Representation (QueryIR) and a pluggable renderer that supports both SQL and GraphQL through a single seven‑stage detection pipeline. In deterministic mode, it achieves 100% execution accuracy with a median latency of 3.2 ms, while the LLM‑backed mode reaches 62‑70% exact match and 84‑91% execution accuracy on benchmark samples.
arXiv:2607. 18241v1 Announce Type: new Abstract: Large language models (LLMs) excel at analyzing individual documents but break down on exhaustive, cross-entity analytical questions over enterprise-scale datasets due to context overflow, loss of per-entity attribution, and linear latency from sequential tool calls.
By Anupreet Walia
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