Model Tailor: Mitigating Catastrophic Forgetting in Multi-modal Large Language Models
<|reference_start|>Model Tailor: Mitigating Catastrophic Forgetting in Multi-modal Large Language Models: Catastrophic forgetting emerges as a critical challenge when fine-tuning multi-modal large language models (MLLMs), where improving performance on unseen tasks often leads to a significant performance drop on the original tasks. This paper presents a comprehensive analysis of catastrophic forgetting in MLLMs and introduces a post-training adjustment method called Model Tailor. Our method primarily preserves the pre-trained parameters while replacing a small number ($\leq$ 10\%) of fine-tuned parameters, maintaining $\sim$ 99\% effectiveness on original tasks versus pre-training, and achieving $\sim$ 97\% on new tasks compared to standard fine-tuning. Specifically, we derive a sparse mask to identify the "model patch", based on a fusion strategy that integrates salience and sensitivity analysis. Subsequently, a compensation mechanism is introduced to "decorate the patch", enhancing the model's performance on both target and original tasks. Additionally, our method is adaptable to multi-task scenarios. Through extensive experiments on InstructBLIP and LLaVA-1.5 in both image captioning and visual question answering tasks, our approach demonstrates significant task adaptability while preserving inherent pre-trained capabilities.<|reference_end|>
@article{zhu2024model,
    title={Model Tailor: Mitigating Catastrophic Forgetting in Multi-modal Large
  Language Models},
    author={Didi Zhu, Zhongyi Sun, Zexi Li, Tao Shen, Ke Yan, Shouhong Ding, Kun
  Kuang, Chao Wu},
    journal={arXiv preprint arXiv:2402.12048},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.12048},
    primaryClass={cs.CL}
}