Correct When Paired, Wrong When Split: Decoupling and Editing Modality-Specific Neurons in MLLMs
Researchers identify a decoupling failure in MLLMs where knowledge updates fail when models are queried with unimodal inputs instead of multimodal pairs.
Current knowledge editing techniques in MLLMs often fail to generalize across modalities. The study shows that while entity knowledge is successfully updated for image-text queries, the model reverts to outdated information when prompted with text alone. This suggests that modality-specific neurons are not being effectively synchronized during the editing process.