  This paper introduces a novel Parameter-Efficient Fine-Tuning (PEFT)
framework for multi-modal, multi-task transfer learning with pre-trained
language models. PEFT techniques such as LoRA, BitFit and IA3 have demonstrated
comparable performance to full fine-tuning of pre-trained models for specific
downstream tasks, all while demanding significantly fewer trainable parameters
and reduced GPU memory consumption. However, in the context of multi-modal
fine-tuning, the need for architectural modifications or full fine-tuning often
becomes apparent. To address this we propose Context-PEFT, which learns
different groups of adaptor parameters based on the token's domain. This
approach enables LoRA-like weight injection without requiring additional
architectural changes. Our method is evaluated on the COCO captioning task,
where it outperforms full fine-tuning under similar data constraints while
simultaneously offering a substantially more parameter-efficient and
computationally economical solution.
