SkelDPO improves code generation efficiency by 2-7% over prior DPO methods via joint preference losses on full code and efficiency-critical skeletons.
Large language models for energy-efficient code: Emerging results and future directions
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EffiSkel improves LLM-generated code efficiency by supervising on extracted structural efficiency skeletons via multi-task learning of code generation and skeleton prediction.
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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SkelDPO: A Skeleton-Guided Direct Preference Optimization Framework for Efficient Code Generation
SkelDPO improves code generation efficiency by 2-7% over prior DPO methods via joint preference losses on full code and efficiency-critical skeletons.
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Chiseling Out Efficiency: Structured Skeleton Supervision for Efficient Code Generation
EffiSkel improves LLM-generated code efficiency by supervising on extracted structural efficiency skeletons via multi-task learning of code generation and skeleton prediction.
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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.