  Multimodal Large Language Models (MLLMs) have demonstrated remarkable
proficiency in diverse tasks across different domains, with an increasing focus
on improving their zero-shot generalization capabilities for unseen multimodal
tasks. Multimodal instruction tuning has emerged as a successful strategy for
achieving zero-shot generalization by fine-tuning pre-trained models on diverse
multimodal tasks through instructions. As MLLMs grow in complexity and size,
the need for parameter-efficient fine-tuning methods like Low-Rank Adaption
(LoRA), which fine-tunes with a minimal set of parameters, becomes essential.
However, applying LoRA in multimodal instruction tuning presents the challenge
of task interference, which leads to performance degradation, especially when
dealing with a broad array of multimodal tasks. To address this, this paper
introduces a novel approach that integrates multimodal instruction tuning with
Conditional Mixture-of-LoRA (MixLoRA). It innovates upon LoRA by dynamically
constructing low-rank adaptation matrices tailored to the unique demands of
each input instance, aiming to mitigate task interference. Experimental results
on various multimodal evaluation datasets indicate that MixLoRA not only
outperforms the conventional LoRA with the same or even higher ranks,
demonstrating its efficacy and adaptability in diverse multimodal tasks.
