Supervised fine-tuning (SFT)  is a crucial step for large language models (LLMs) , enabling them to align with human instructions and enhance their capabilities in downstream tasks.
When the models are required to align with a broader range of downstream tasks, or there is a desire to notably improve the performance on a specific task, a substantial increase in fine-tuning data often emerges as the solution.
However, we find that large-scale increases in instruction data can disrupt the world knowledge previously stored in the LLMs, i.e., world knowledge forgetting.
In this paper, we introduce LoRAMoE to address the above challenge.
The LoRAMoE is a plugin version of Mixture of Experts (MoE) .
The plugin form ensures the integrity of world knowledge by freezing the backbone model during the training phase.
We then propose the use of localized balancing constraints to coordinate parts of experts for task utilization, meanwhile enabling other experts to fully leverage the world knowledge stored in the models.
Experimental results demonstrate that LoRAMoE can reasonably coordinate experts based on data type during inference, and even dramatically increasing instruction data does not result in knowledge forgetting.
Moreover, LoRAMoE provides additional benefits for the performance of downstream tasks, indicating the potential of our approach for multi-task learning.