"Prompt engineering and its implications on the energy consumption of Large Language Models"

Prompt engineering with custom tags cuts LLM energy use by up to 99% in code completion tasks.

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https://arxiv.org/abs/2501.05899

Original Problem 🤔:

Training and using LLMs for code-related tasks consumes massive computational resources and energy, contributing significantly to carbon emissions. Measuring and reducing this environmental impact is challenging due to complex infrastructure requirements and lack of standardized evaluation methods.

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Solution in this Paper 🛠️:

→ The researchers introduced custom tags in prompts to distinguish different components like input code and completion targets

→ They tested five distinct prompt configurations with varying levels of tag usage and explanations

→ The study evaluated three prompting techniques: zero-shot, one-shot, and few-shots

→ They used CodeXGLUE dataset with 1,000 Java code snippets for comprehensive testing

→ Energy consumption was measured using CodeCarbon tool on an isolated testing environment

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Key Insights 💡:

→ Custom tags in prompts can significantly reduce energy consumption without compromising accuracy

→ System role specifications in prompts impact both energy usage and model performance

→ Few-shots prompting with custom tags shows the best balance of efficiency and accuracy

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Results 📊:

→ Zero-shot prompting achieved 7% energy reduction

→ One-shot prompting showed 99% decrease in energy consumption

→ Few-shots configuration reduced energy usage by 83%

→ Maintained or improved accuracy metrics across all configurations