NeuroAda: Activating Each Neuron's Potential for Parameter-Efficient
  Fine-Tuning
Authors: name: Zhi Zhang, name: Yixian Shen, name: Congfeng Cao, name: Ekaterina Shutova
Organization: University of Amsterdam

Existing parameter-efficient fine-tuning (PEFT) methods primarily fall into
two categories: addition-based and selective in-situ adaptation. The former,
such as LoRA, introduce additional modules to adapt the model to downstream
tasks, offering strong memory efficiency. However, their representational
capacity is often limited, making them less suitable for fine-grained
adaptation. In contrast, the latter directly fine-tunes a carefully chosen
subset of the original model parameters, allowing for more precise and
effective adaptation, but at the cost of significantly increased memory
consumption. To reconcile this trade-off, we propose NeuroAda, a novel PEFT
method that enables fine-grained model finetuning while maintaining high memory
efficiency. Our approach first identifies important parameters (i.e.,
connections within the network) as in selective adaptation, and then introduces
bypass connections for these selected parameters. During finetuning, only the
bypass connections are updated, leaving the original model parameters frozen.
Empirical results on 23+ tasks spanning both natural language generation and
understanding demonstrate that NeuroAda achieves state-of-the-art performance
with as little as leq 0.02% trainable parameters, while reducing
CUDA memory usage by up to 60%. We release our code here:
https://github.com/FightingFighting/NeuroAda.git.