Generative large language models (LLMs) exhibit impressive capabilities, which can be further augmented by integrating a pre-trained vision model into the original LLM to create a multimodal LLM (MLLM). However, this integration often significantly decreases performance on natural language understanding and generation tasks, compared to the original LLM. This study investigates this issue using the LLaVA MLLM, treating the integration as a continual learning problem. We evaluate five continual learning methods to mitigate forgetting and identify a technique that enhances visual understanding while minimizing linguistic performance loss. Our approach reduces linguistic performance degradation by up to 15\% over the LLaVA recipe, while maintaining high multimodal accuracy. We also demonstrate the robustness of our method through continual learning on a sequence of vision-language tasks, effectively preserving linguistic skills while acquiring new multimodal capabilities.

本研究解决了多模态大型语言模型在整合预训练视觉模型后，语言理解与生成任务性能显著下降的问题。提出将整合视为持续学习问题，并评估五种方法以减轻遗忘现象，从而找到一种减少语言性能损失的技术，最终使语言性能下降幅度最多降低15%，同时保持高效的多模态准确性。 

通过持续学习改善多模态大型语言模型