The purpose of the Text-to-SQL task is to bridge the gap between natural language and SQL queries. Current approaches mainly rely on large language models (LLMs), but employing them for Text-to-SQL ha ...
Data work in 2026 asks for more than chart building. Professionals are expected to clean data, query databases, explain trends, and present findings clearly across business, finance, product, and ...
Abstract: Current Text-to-SQL methods are evaluated and only focused on executable queries overlooking the semantic alignment challenge both in terms of the semantic meaning of the query and the ...
Spider is a large human-labeled dataset for complex and cross-domain semantic parsing and text-to-SQL task (natural language interfaces for relational databases). It is released along with our EMNLP ...
Spider is a large human-labeled dataset for complex and cross-domain semantic parsing and text-to-SQL task (natural language interfaces for relational databases). It is released along with our EMNLP ...
Every data engineering team right now is being asked the same question: "How do we build a chatbot that talks to our data?" The prototypes are deceptively simple. A developer connects GPT-5.1 to a ...
A team of AI researchers at Bloomberg have developed PExA, an agentic framework that achieved 70.2% execution accuracy, sharing one of the top positions on the Spider 2.0 (Snow) leaderboard, one of ...
In most enterprises, data access still feels like a locked room with SQL as the only key. Business teams depend on data engineers for every report, dashboard, or metric tweak. Even in the age of ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results