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arXiv Computation and Language By Fengli Wu, Vaidehi Patil, Jaehong Yoon, Yue Zhang, Mohit Bansal

Hierarchy-Aware Multimodal Unlearning for Medical AI

Read the original on arXiv Computation and Language →

The paper introduces MedForget, a hierarchy‑aware multimodal unlearning benchmark that models medical data as nested institutional structures, allowing fine‑grained evaluation of retain and forget splits. Experiments reveal that existing unlearning methods struggle to achieve effective hierarchy‑aware forgetting without harming retained utility on clinically relevant tasks. To address this, the authors propose CHIP, a training‑free method that deletes target‑specific weight subspaces while preserving sibling‑shared information, achieving superior forget‑retain performance across all hierarchy levels while maintaining competitive downstream utility.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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