An end-to-end energy measurement framework for LLM distillation pipelines reveals hidden teacher-side costs and yields selection guidelines plus an open-source harness.
Proceedings of the 57th annual meeting of the association for computational linguistics , pages=
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A graph-spectral importance score based on layer-wise structural distortion between pre- and post-activation neuron graphs identifies removable neurons for iterative pruning without intermediate updates, followed by recovery fine-tuning.
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Towards Resource-Efficient LLMs: End-to-End Energy Accounting of Distillation Pipelines
An end-to-end energy measurement framework for LLM distillation pipelines reveals hidden teacher-side costs and yields selection guidelines plus an open-source harness.
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Spectral structural distortion reveals redundant neurons in neural networks
A graph-spectral importance score based on layer-wise structural distortion between pre- and post-activation neuron graphs identifies removable neurons for iterative pruning without intermediate updates, followed by recovery fine-tuning.