4 papers · 1 filter
RefusalGuard: Geometry-Preserving Fine-Tuning for Safety in LLMs
Sadia Asif, Mohammad Mohammadi Amiri
Fine-tuning safety-aligned language models for downstream tasks often leads to substantial degradation of refusal behavior, making models vulnerable to adversarial misuse. While pr…
WIN-U: Woodbury-Informed Newton-Unlearning as a retain-free Machine Unlearning Framework
Xingjian Zhao, Mohammad Mohammadi Amiri, Malik Magdon-Ismail
Privacy concerns in LLMs have led to the rapidly growing need to enforce a data's "right to be forgotten". Machine unlearning addresses precisely this task, namely the removal of t…
Towards Reversible Model Merging For Low-rank Weights
Mohammadsajad Alipour, Mohammad Mohammadi Amiri
Model merging aims to combine multiple fine-tuned models into a single set of weights that performs well across all source tasks. While prior work has shown that merging can approx…
Power to the Clients: Federated Learning in a Dictatorship Setting
Mohammadsajad Alipour, Mohammad Mohammadi Amiri
Federated learning (FL) has emerged as a promising paradigm for decentralized model training, enabling multiple clients to collaboratively learn a shared model without exchanging t…