MemEIC: A Step Toward Continual and Compositional Knowledge Editing
Jin Seong, Jiyun Park, Wencke Liermann, Hongseok Choi, Yoonji Nam, Hyun Kim, Soojong Lim, Namhoon Lee

TL;DR
MemEIC introduces a novel method for continual and compositional knowledge editing in large vision-language models, enabling sequential multimodal updates with improved performance and knowledge preservation.
Contribution
It proposes MemEIC, a hybrid external-internal editing framework with dual memories and adapters for disentangled, multimodal knowledge updates in LVLMs.
Findings
Enhanced performance on complex multimodal questions
Effective preservation of prior knowledge edits
Sets new benchmarks for CCKE in LVLMs
Abstract
The dynamic nature of information necessitates continuously updating large vision-language models (LVLMs). While recent knowledge editing techniques hint at promising directions, they often focus on editing a single modality (vision or language) in isolation. This prevalent practice neglects the inherent multimodality of LVLMs and the continuous nature of knowledge updates, potentially leading to suboptimal editing outcomes when considering the interplay between modalities and the need for ongoing knowledge refinement. To address these limitations, we propose MemEIC, a novel method for Continual and Compositional Knowledge Editing (CCKE) in LVLMs. MemEIC enables compositional editing of both visual and textual knowledge sequentially. Our approach employs a hybrid external-internal editor featuring a dual external memory for cross-modal evidence retrieval and dual LoRA adapters that…
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