  Parameter-efficient tuning methods such as LoRA could achieve comparable
performance to model tuning by tuning a small portion of the parameters.
However, substantial computational resources are still required, as this
process involves calculating gradients and performing back-propagation
throughout the model. Much effort has recently been devoted to utilizing the
derivative-free optimization method to eschew the computation of gradients and
showcase an augmented level of robustness in few-shot settings. In this paper,
we prepend the low-rank modules into each self-attention layer of the model and
employ two derivative-free optimization methods to optimize these low-rank
modules at each layer alternately. Extensive results on various tasks and
language models demonstrate that our proposed method achieves substantial
improvement and exhibits clear advantages in memory usage and convergence speed
compared to existing gradient-based parameter-efficient tuning and
derivative-free optimization methods in few-shot settings.
