  Automatic SQL generation has been an active research area, aiming at
streamlining the access to databases by writing natural language with the given
intent instead of writing SQL. Current SOTA methods for semantic parsing depend
on LLMs to achieve high predictive accuracy on benchmark datasets. This reduces
their applicability, since LLMs requires expensive GPUs. Furthermore, SOTA
methods are ungrounded and thus not guaranteed to always generate valid SQL.
Here we propose T5QL, a new SQL generation method that improves the performance
in benchmark datasets when using smaller LMs, namely T5-Base, by 13pp when
compared against SOTA methods. Additionally, T5QL is guaranteed to always
output valid SQL using a context-free grammar to constrain SQL generation.
Finally, we show that dividing semantic parsing in two tasks, candidate SQLs
generation and candidate re-ranking, is a promising research avenue that can
reduce the need for large LMs.
