PhD student, University of Cambridge
2 papers at NeurIPS 2025
We propose a new class of fine-tuned LLMs, Permissioned LLMs, that enforce access control on responses to queries, thus protecting sensitive training/tuning data from unauthorized queries.
We propose LUNAR, an novel LLM unlearning method by redirecting the representation of unlearned data for effective, controlled and efficient unlearning.
