  The current state-of-the-art (SOTA) for automated text-to-SQL still falls
well short of expert human performance as measured by execution accuracy (EX)
on the BIRD-SQL benchmark. The most accurate methods are also slow and
expensive. To advance the SOTA for text-to-SQL while reducing cost and
improving speed, we explore the combination of low-cost fine tuning, novel
methods for diverse retrieval-augmented generation (RAG) and new input and
output formats that help large language models (LLMs) achieve higher EX. We
introduce two new methods, Dubo-SQL v1 and v2. Dubo-SQL v1 sets a new record
for EX on the holdout test set of BIRD-SQL. Dubo-SQL v2 achieves even higher
performance on the BIRD-SQL dev set. Dubo-SQL v1 relies on LLMs from OpenAI,
but uses the low-cost GPT-3.5 Turbo while exceeding the performance of the
next-best model using OpenAI, which instead uses the more expensive GPT-4.
Dubo-SQL v1 exceeds the performance of the next-best model using GPT-3.5 by
over 20%. Dubo-SQL v2 uses GPT-4 Turbo and RAG in place of fine tuning to push
EX higher.
