The instruction tuning approach used in MVLLaVA presents several potential limitations and trade-offs. One significant limitation is the reliance on the quality and diversity of the instruction templates. If the templates are not comprehensive enough to cover the wide range of user queries, the model may struggle to interpret and respond accurately to novel or unexpected instructions. This could lead to a decrease in performance, particularly in edge cases or less common tasks.
Another trade-off is the computational cost associated with fine-tuning the large multimodal model. While instruction tuning can enhance the model's performance, it may also require substantial computational resources and time, especially when adapting to new tasks or input types. This could limit the model's scalability and accessibility for users with limited resources.
To address these limitations, future work could focus on developing more robust and adaptive instruction templates that can dynamically adjust based on user input. Implementing a feedback mechanism where the model learns from user interactions could help refine the instruction templates over time, improving accuracy and user satisfaction. Additionally, exploring more efficient fine-tuning techniques, such as few-shot or zero-shot learning, could reduce the computational burden while maintaining high performance across diverse tasks.