一项新论文通过分析提示词属性（如认知负荷和措辞模式）如何影响推理过程中的能耗，来探讨设备端大语言模型（LLMs）的能耗问题。研究发现，认知负荷会影响每 token 的能耗成本，而措辞模式主要通过 token 数量影响能耗。这项研究强调了模型感知提示词设计对于实现设备端大语言模型应用能效的重要性。 AI
影响 强调了设备端大语言模型提示词复杂性与能效之间的权衡。
排序理由 关于大语言模型能耗的学术论文。
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论文分析提示词对设备端大语言模型能耗的影响
本文如何被排名
- Signal score
- 22 / 100
- Composite score across the factors below. Higher = stronger signal that this story matters right now.
- Newsworthiness bucket
- Tool
- 关于大语言模型能耗的学术论文。
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- Topics
- paper, infra
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完整方法见我们的编辑标准。
报道来源 [1]
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提示词变化如何影响设备端大语言模型的能耗？
arXiv:2609.01798v1 Announce Type: new 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 prom…