Chain-of-Thought prompting balances high accuracy with low energy use in small language models for code generation, while multi-sampling strategies add high energy costs for small accuracy gains.
Learn to code sustainably: An empirical study on llm-based green code generation
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Contrastive Prompt Tuning raises code accuracy on two of three tested models but produces inconsistent energy-efficiency gains that depend on model, language, and task.
A systematic review finds research on the sustainability of LLM-generated code to be limited, fragmented, and without accepted frameworks for measurement or benchmarking.
citing papers explorer
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Evaluating the Environmental Impact of using SLMs and Prompt Engineering for Code Generation
Chain-of-Thought prompting balances high accuracy with low energy use in small language models for code generation, while multi-sampling strategies add high energy costs for small accuracy gains.
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An Initial Exploration of Contrastive Prompt Tuning to Generate Energy-Efficient Code
Contrastive Prompt Tuning raises code accuracy on two of three tested models but produces inconsistent energy-efficiency gains that depend on model, language, and task.
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Sustainable Code Generation Using Large Language Models: A Systematic Literature Review
A systematic review finds research on the sustainability of LLM-generated code to be limited, fragmented, and without accepted frameworks for measurement or benchmarking.