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Neural Architecture Search for Parameter-Efficient Fine-tuning of Large Pre-trained Language Models
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Parameter-efficient tuning (PET) methods fit pre-trained language models (PLMs) to downstream tasks by either computing a small compressed update for a subset of model parameters, or appending and fine-tuning a small number of new model parameters to the pre-trained network. Hand-designed PET architectures from the literature perform well in practice, but have the potential to be improved via automated neural architecture search (NAS). We propose an efficient NAS method for learning PET architectures via structured and unstructured pruning. We present experiments on GLUE demonstrating the effectiveness of our algorithm and discuss how PET architectural design choices affect performance in practice.
Forward citations
Cited by 2 Pith papers
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SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling
A LoRA update split into several fixed, differently-scaled low-rank experts with orthogonal input directions improves fine-tuning accuracy at the same parameter count.
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PrunePEFT: Iterative Hybrid Pruning for Parameter-Efficient Fine-tuning of LLMs
An iterative hybrid pruning method selects which PEFT modules to keep at each transformer layer, matching or improving fixed PEFT baselines on GLUE at 1% trainable parameters.
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