Dubo-SQL: Diverse Retrieval-Augmented Generation and Fine Tuning for Text-to-SQL
This work addresses the gap between automated text-to-SQL systems and expert human performance, offering a more cost-effective and faster solution for database query generation.
The paper tackles the problem of improving text-to-SQL performance by combining low-cost fine-tuning and diverse retrieval-augmented generation, resulting in Dubo-SQL v1 setting a new record for execution accuracy on the BIRD-SQL benchmark and exceeding the next-best GPT-3.5 model by over 20%.
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.