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2024

Blar-SQL: Faster, Stronger, Smaller NL2SQL

Domínguez, José Manuel, Errázuriz, Benjamín, Daher, Patricio

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Large Language Models (LLMs) have gained considerable notoriety in the field of natural language to SQL tasks (NL2SQL).

In this study, we show how task decomposition can greatly benefit LLMs in database understanding and query generation in order to answer human questions with an SQL query.
We fined-tuned open source models, specifically Llama-2 and Code Llama, by combining 2 different models each designated to focus on one of two tasks in order to leverage each model's core competency to further increase the accuracy of the final SQL query.
We propose a new framework to divide the schema into chunks in order to fit more information into a limited context.

Blar-SQL: Faster, Stronger, Smaller NL2SQL · Around

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Seq2sql: Generating structured queries from natural language using reinforcement learning
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Victor Zhong, Caiming Xiong, and Richard Socher. 2017 · 2017
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Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task
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Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, et al · 2018
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Decomposed prompting: A modular approach for solving complex tasks
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Tushar Khot, Harsh Trivedi, Matthew Finlayson, Yao Fu, Kyle Richardson, Peter Clark, and Ashish Sabharwal. 2022 · 2022
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