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.
Energy-aware code generation with llms: Benchmarking small vs. large language models for sustainable ai programming
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EcoAssist embeds energy estimation and optimization into AI-assisted frontend coding, reducing website energy use by 13-16% in benchmarks while preserving developer productivity.
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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EcoAssist: Embedding Sustainability into AI-Assisted Frontend Development
EcoAssist embeds energy estimation and optimization into AI-assisted frontend coding, reducing website energy use by 13-16% in benchmarks while preserving developer productivity.
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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.