כתבה
arXiv cs.AI ·
שיפור יעילות האנרגיה של קוד LLMs
Improving the Energy-Efficiency of the Code Generated by LLMs through Effective Prompting
חוקרים בדקו 21 אסטרטגיות עבור יצירת קוד אנרגטי עם LLMs. התוצאות הראו חיסכון של עד 25% באנרגיה עבור קוד Python ו-17% עבור C++.
תקציר מקורי באנגליתarXiv:2610.02571v1 Announce Type: cross Abstract: As AI-assisted programming becomes increasingly mainstream, the environmental impact of AI-generated software has emerged as an important consideration. This motivates evaluating LLM-generated code beyond functional correctness by considering execution efficiency and energy consumption. However, despite substantial advances in code generation, frontier LLMs are rarely evaluated based on the energy efficiency of the code they produce. In this work, we conduct a comprehensive evaluation of 21 prompting strategies for energy-efficient code generation and identify 8 strategies for evaluation across 10 widely used open-weight and proprietary LLMs. We evaluate their effectiveness for both Python and C++ code generation relative to a baseline prom
קרא במקור המקורי
arxiv.org
פתח כתבה מקורית