通过保留跨模态对齐轨迹，解决多模态模型微调中的遗忘问题。
LLaVAFlow: Preserving Latent Alignment Flow for Parameter-Efficient Multimodal Fine-Tuning
- 基于信息论设计对齐流压缩机制，动态保持跨模态关系。
- 在多个下游任务上提升性能，通用性优于现有方法10%以上。
- 适合需要高效微调且不希望丢失原模型能力的研究者。
尽管多模态大语言模型（MLLMs）具备强泛化能力，但在下游任务的视觉指令微调过程中不可避免地引发灾难性遗忘，损害整体泛化性能。现有方法虽通过限制权重更新来缓解遗忘，却忽视了MLLM中隐含的跨模态对齐。基于前期工作与观察，我们提出跨模态对齐信息内嵌于信息压缩轨迹中。为此，我们提出LLaVAFlow——一种信息论蒸馏框架：首先，压缩提取关系与MLLM嵌入间的互信息，使可学习模块生成优化的对齐流以支持下游任务；其次，最大化预训练与微调后MLLM对齐流之间的互信息，实现紧凑对齐信息的迁移。大量实验表明，LLaVAFlow是一种高效的即插即用框架，能有效保留对齐流，提升下游性能与泛化能力。
原文摘要 · Abstract (English)
While Multimodal Large Language Models (MLLMs) exhibit strong generalization, visual instruction tuning for downstream tasks inevitably causes catastrophic forgetting, impairing overall generalization. While existing methods regulate weight updates to reduce forgetting, they overlook the fundamental cross-modal alignment in MLLMs. Based on prior work and our observations, we argue that cross-modal alignment is implicitly captured in the information-compression trajectory. To preserve the alignment flow embedded in the trajectory, we propose LLaVAFlow, an information-theoretic distillation framework. First, we compress the mutual information between the extracted relations and MLLM embeddings, encouraging a learnable module to produce a refined alignment flow that benefits downstream tasks. Second, we maximize the mutual information between the extracted alignment flows of the pretrained and fine-tuned MLLMs, enabling the transfer of compact alignment information. Extensive experiments show that LLaVAFlow is an effective plug-and-play framework that preserves alignment flow and enhances both downstream performance and generalization.
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