Can LLMs substitute SQL? Comparing Resource Utilization of Querying LLMs versus Traditional Relational Databases

arXiv:2404.08727v1 Announce Type: cross
Abstract: Large Language Models (LLMs) can automate or substitute different types of tasks in the software engineering process. This study evaluates the resource utilization and accuracy of LLM in interpreting and executing natural language queries against traditional SQL within relational database management systems. We empirically examine the resource utilization and accuracy of nine LLMs varying from 7 to 34 Billion parameters, including Llama2 7B, Llama2 13B, Mistral, Mixtral, Optimus-7B, SUS-chat-34B, platypus-yi-34b, NeuralHermes-2.5-Mistral-7B and Starling-LM-7B-alpha, using a …

abstract accuracy arxiv automate cs.ai cs.cl cs.db database database management databases engineering language language models large language large language models llm llms management natural natural language natural language queries process queries relational relational database relational databases software software engineering sql study tasks type types