Instruction tuning represents a prevalent strategy employed by Multimodal Large Language Models (MLLMs) to align with human instructions and adapt to new tasks. Nevertheless, MLLMs encounter the challenge of adapting to users' evolving knowledge and demands. Therefore, how to retain existing skills while acquiring new knowledge needs to be investigated. In this paper, we present a comprehensive benchmark, namely Continual Instruction tuNing (CoIN), to assess existing MLLMs in the sequential instruction tuning paradigm. CoIN comprises 10 commonly used datasets spanning 8 task categories, ensuring a diverse range of instructions and tasks. Besides, the trained model is evaluated from two aspects: Instruction Following and General Knowledge, which assess the alignment with human intention and knowledge preserved for reasoning, respectively. Experiments on CoIN demonstrate that current powerful MLLMs still suffer catastrophic forgetting, and the failure in intention alignment assumes the main responsibility, instead of the knowledge forgetting. To this end, we introduce MoELoRA to MLLMs which is effective to retain the previous instruction alignment. Experimental results consistently illustrate the forgetting decreased from this method on CoIN.

翻译：指令微调是多模态大语言模型（MLLMs）为对齐人类指令并适应新任务而广泛采用的策略。然而，MLLMs面临如何适应人类持续演变的知识与需求的挑战。因此，如何在获取新知识的同时保留既有技能亟需研究。本文提出一个综合性基准——持续指令微调（CoIN），用于评估现有MLLMs在序列式指令微调范式下的表现。CoIN包含覆盖8类任务的10个常用数据集，确保指令与任务的多样性。此外，训练后的模型从指令遵循与通用知识两个维度进行评估，分别衡量其与人类意图的对齐程度及推理所需知识的保留能力。CoIN上的实验表明，当前强大的MLLMs仍存在灾难性遗忘问题，且意图对齐失效是主要诱因，而非知识遗忘。为此，我们将MoELoRA引入MLLMs，该方法能有效保留先前的指令对齐能力。实验结果一致证明，该方法在CoIN上显著降低了遗忘程度。