Model Tailor: Mitigating Catastrophic Forgetting in Multi-modal Large Language Models
This addresses the problem of performance degradation in multi-modal AI models for researchers and practitioners, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles catastrophic forgetting in multi-modal large language models during fine-tuning by introducing Model Tailor, a post-training adjustment method that replaces up to 10% of fine-tuned parameters, maintaining ~99% effectiveness on original tasks and achieving ~97% on new tasks compared to standard fine-tuning.
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.