Energy-Aware Data-Driven Model Selection in LLM-Orchestrated AI Systems

This article likely discusses a research paper focused on optimizing the selection of models within AI systems orchestrated by Large Language Models (LLMs). The core focus is on energy efficiency, suggesting the research explores methods to choose models that minimize energy consumption while maintaining performance. The use of data-driven methods implies the research leverages data to inform model selection, potentially through training or analysis of model characteristics.