关键词很重要：揭示设备端大语言模型提示的能量敏感性
Keyword Matters: Unveiling the Energy Sensitivity of On-Device LLM Prompting
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中文总结 AI 辅助
研究设备端大语言模型提示措辞与能耗关系，通过在智能手机上收集功率测量数据，量化语言特征对解码长度和总能量的影响，发现提示工程是提高能效的有效手段。
中文摘要 AI 辅助
大语言模型（LLMs）越来越多地部署在移动和嵌入式设备上，以提高隐私性和减少网络延迟。但设备端推理面临高能耗问题。本文对设备端大语言模型提示措辞与能耗之间的关系进行实证研究。通过在智能手机上收集实际功率测量数据，量化语言特征对解码长度和总能量的影响。结果表明不同动词和任务存在能量差异，提示工程是提高能效的有效手段。
英文摘要
Large Language Models (LLMs) are increasingly deployed on mobile and embedded devices to improve privacy and reduce network latency. Yet on-device inference faces a fundamental constraint: high energy consumption on battery-powered, resource-limited hardware. While model compression and runtime acceleration have been widely studied, the effect of \emph{prompt design} on energy efficiency remains underexplored. This paper presents an empirical study of the relationship between prompt wording and energy consumption for on-device LLMs. Using real power measurements collected on a smartphone, we quantify how linguistic features, particularly imperative keywords and instruction structure, affect decoding length and total energy. Our results show consistent energy differences across verbs and tasks, indicating that prompt engineering is a lightweight lever for improving energy efficiency.
发表机构
- Georgia State University（佐治亚州立大学）
机构由 AI 辅助整理，请以论文原文为准。
