multiple source tasks to initialize the soft prompt of the
target task, based on the similarity between the source and
target
tasks, or employ multi-task data to learn a single
shared prompt and transfer it to the target task. Similar to
the adapter, a composition of multiple task-specific LoRA
modules is also leveraged to transfer knowledge to new tasks
[55], [56]. L-LoRA [57] enhances the fusion capabilities of
multi-task learning by preventing negative inference between
task-specific representations. Additionally, [93] utilizes arith-
metic operators, such as the addition and negation operators,
to merge parameters of various PEFT methods trained on
different tasks for multi-task learning.

B. Cross-Lingual Transfer
Parameter-EfficientFine-TuningMethods