  Parameter-efficient fine-tuning stands as the standard for efficiently
fine-tuning large language and vision models on downstream tasks. Specifically,
the efficiency of low-rank adaptation has facilitated the creation and sharing
of hundreds of custom LoRA modules, each trained on distinct data from various
downstream tasks. In this paper, we explore the composability of LoRA modules,
examining if combining these pre-trained modules enhances generalization to
unseen downstream tasks. Our investigation involves evaluating two approaches:
(a) uniform composition, involving averaging upstream LoRA modules with equal
weights, and (b) learned composition, where we learn the weights for each
upstream module and perform weighted averaging. Our experimental results on
both vision and language models reveal that in few-shot settings, where only a
limited number of samples are available for the downstream task, both uniform
and learned composition methods result in better transfer accuracy;
outperforming full fine-tuning and training a LoRA from scratch. Moreover, in
full-shot settings, learned composition performs comparably to regular LoRA
training with significantly fewer number of trainable parameters. Our research
unveils the potential of uniform composition for enhancing transferability in
low-shot settings, without introducing additional learnable parameters.
