Problem
Research questions and friction points this paper is trying to address.
energy consumption
prompt design
on-device LLMs
cognitive load
phrasing pattern
How Do Prompt Variations Affect Energy Consumption in On-Device LLMs?
🤖 AI Summary
研究通过实验分析了提示设计中的认知负荷和表述模式如何影响设备上大型语言模型的能耗，提出了模型感知的提示设计以提高能效。
📝 Abstract
Large language models (LLMs) are increasingly deployed on mobile devices, making energy efficiency a key deployment constraint, yet the energy impact of prompt design remains underexplored. This paper aims to understand how two prompt properties, cognitive load and phrasing pattern, shape the energy behavior of on-device LLM inference. We conduct a broad empirical study covering prompt properties, datasets, models, and devices, with phase-level profiling that separates prefill and decode energy. We find that cognitive load primarily affects the energy cost per token, while phrasing pattern affects energy largely through token usage. Our energy-quality analysis further shows that prompt design reshapes the attainable frontier differently across models, highlighting the need for model-aware prompt design in energy-efficient on-device LLM inference. Code, datasets, and scripts are available at https://amai-gsu.github.io/PromptProperty/.
Innovation
Methods, ideas, or system contributions that make the work stand out.
cognitive load
phrasing pattern
energy efficiency
on-device LLMs
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