GEM selects fine-tuning parameters by gradient-to-weight ratio and distributes the budget by layer entropy, reaching 0.1% parameter updates with small accuracy gains on several NLP tasks.
Boolq: Exploring the surprising difficulty of natural yes/no questions
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GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation
GEM selects fine-tuning parameters by gradient-to-weight ratio and distributes the budget by layer entropy, reaching 0.1% parameter updates with small accuracy gains on several NLP tasks.