We consider the problem of neural semantic parsing, which translates natural language questions into executable SQL queries. We introduce a new mechanism, execution guidance, to leverage the semantics ...

We present a neural approach called IRNet for complex and cross-domain Text-to-SQL. IRNet aims to address two challenges: 1) the mismatch between intents expressed in natural language (NL) and the ...

An icon in the shape of a lightning bolt. Impact Link The right-click has revolutionized how we use our computers. In one swift motion, you can summon the most applicable shortcuts and functions ...

This repo contains a diagnostic evaluation benchmark toward the robustness of text-to-SQL models, which contains 17 perturbation test sets to measure the robustness of models from different angles. It ...

Move to Chat page to choose which working mode to use. The explanation of working modes are explained in Architecture section above.. Fill in details of SQLite file or PostgresSQL server if using ...

Why text-to-SQL isn’t a solved problem (yet) for enterprise AI and data. Multiple LLMs could generate SQL from basic natural language queries. So why bother to create yet another text-to-SQL model?

“Text-to-SQL” systems that rely on AI have become increasingly popular – even standalone AI chatbots, such as OpenAI’s ChatGPT, can generate SQL code that can be plugged into such databases.

Gretel’s synthetic Text-to-SQL dataset outperforms the b-mc2/sql-create-context dataset across various grading criteria, including compliance with SQL standards (+54.6%), ...