AINeutralarXiv – CS AI · Apr 64/10
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An Initial Exploration of Contrastive Prompt Tuning to Generate Energy-Efficient Code
Researchers explored using Contrastive Prompt Tuning (CPT) to improve Large Language Models' ability to generate energy-efficient code, combining contrastive learning with parameter-efficient fine-tuning. The study tested CPT across Python, Java, and C++ on three different models, finding consistent accuracy improvements for two models but variable efficiency gains depending on model, language, and task complexity.