KORE: Enhancing Knowledge Injection for Large Multimodal Models via
  Knowledge-Oriented Augmentations and Constraints
Authors: name: Kailin Jiang, name: Hongbo Jiang, name: Ning Jiang, name: Zhi Gao, name: Jinhe Bi, name: Yuchen Ren, name: Bin Li, name: Yuntao Du, name: Lei Liu, name: Qing Li
Organization: Beijing Institute for General Artificial Intelligence

Large Multimodal Models encode extensive factual knowledge in their
pre-trained weights. However, its knowledge remains static and limited, unable
to keep pace with real-world developments, which hinders continuous knowledge
acquisition. Effective knowledge injection thus becomes critical, involving two
goals: knowledge adaptation (injecting new knowledge) and knowledge retention
(preserving old knowledge). Existing methods often struggle to learn new
knowledge and suffer from catastrophic forgetting. To address this, we propose
KORE, a synergistic method of KnOwledge-oRientEd augmentations and constraints
for injecting new knowledge into large multimodal models while preserving old
knowledge. Unlike general text or image data augmentation, KORE automatically
converts individual knowledge items into structured and comprehensive knowledge
to ensure that the model accurately learns new knowledge, enabling accurate
adaptation. Meanwhile, KORE stores previous knowledge in the covariance matrix
of LMM's linear layer activations and initializes the adapter by projecting the
original weights into the matrix's null space, defining a fine-tuning direction
that minimizes interference with previous knowledge, enabling powerful
retention. Extensive experiments on various LMMs, including LLaVA-v1.5-7B,
LLaVA-v1.5-13B, and Qwen2.5-VL-7B, show that KORE achieves superior new
knowledge injection performance and effectively mitigates catastrophic
forgetting.