with low computation costs. L-LoRA constructs a linear
function using a fir-order Taylor expansion, as illustrated in
Equation 15. In L-LoRA, only the linearized LoRA modules
are fine-tuned in the tangent space, incurring fewer trainable
parameters compared to LoRA. For the multi-task fusion meth-
ods, simple average, task arithmetic [92], [93], ties-merging
[94], and LoRAhub [55] are employed for multi-task fusion.
Parameter-EfficientFine-TuningMethods