arXiv AI

Schema-First Retrieval: Embedding Catalogs for Natural Language Analytics

arXiv:2606. 28387v1 Announce Type: cross Abstract: Enterprise text-to-SQL systems often fail before SQL is generated: the model receives the wrong schema context.

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.

arXiv AI
Aug 7

Tytan: Interactive Neurosymbolic Construction of Analytic Semantic Schemas from Relational Data

arXiv:2608. 06331v1 Announce Type: cross Abstract: From natural-language query interfaces to automated report generation, data analysis tools need a description of the data: the real-world entities it contains, which columns function as measures or identifiers, and how tables connect into units of analysis.

By Donna Hooshmand, Shubham Shahi, Cameron Barrie, Abhratanu Dutta, Marko Sterbentz, Harper Pack, Kristian J. Hammond
arXiv AI
Jun 16

Bridging the Gap: Enabling Natural Language Queries for NoSQL Databases through Text-to-NoSQL Translation

arXiv:2502. 11201v3 Announce Type: replace-cross Abstract: NoSQL databases are core data infrastructure, yet natural-language access to them remains underdeveloped: correct query generation must recover how a non-relational data model represents entities, nested paths, arrays, missing fields, and dynamic keys.

By Jinwei Lu, Jiawei Lu, Chen Zhang, Zhiqian Qin, Haodi Zhang, Yuanfeng Song, Raymond Chi-Wing Wong