One potential limitation of the asymmetric LoRA architecture is the increased complexity introduced by having multiple B matrices for different intrinsic components. This complexity may lead to higher computational overhead and memory requirements, especially when dealing with a large number of intrinsic components. To address this, future research could focus on optimizing the architecture by exploring techniques for parameter sharing or parameter reduction without compromising performance. Additionally, developing more efficient training algorithms specifically tailored for asymmetric architectures could help mitigate the computational burden associated with multiple B matrices.
Another drawback could be the potential for overfitting or underfitting specific intrinsic components, leading to suboptimal performance on certain tasks. To overcome this, future research could investigate adaptive learning strategies that dynamically adjust the allocation of resources to different intrinsic components based on their importance or relevance to the overall task. By incorporating mechanisms for adaptive resource allocation, the asymmetric LoRA architecture can better adapt to the varying complexities and requirements of different datasets.