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.