5 papers · 1 filter
Behavioral Forgetting Is Not Privacy in Machine Unlearning
Tyler Lizzo, Larry Heck
Machine unlearning aims to remove targeted information from large language models, but existing evaluations often measure forgetting only on a designated benchmark distribution. We…
QR-Erase: Efficient Subspace-Based Machine Unlearning with Layer Localization
Tyler Lizzo, Larry Heck
Machine unlearning seeks to remove targeted information from trained models without requiring costly retraining. Existing optimization-based methods often degrade unrelated capabil…
SNEAK: Evaluating Strategic Communication and Information Leakage in Large Language Models
Adar Avsian, Larry Heck
Large language models (LLMs) are increasingly deployed in multi-agent settings where communication must balance informativeness and secrecy. In such settings, an agent may need to…
Unlearning in LLMs: Methods, Evaluation, and Open Challenges
Tyler Lizzo, Larry Heck
Large language models (LLMs) have achieved remarkable success across natural language processing tasks, yet their widespread deployment raises pressing concerns around privacy, cop…
Evaluating Cross-Lingual Unlearning in Multilingual Language Models
Tyler Lizzo, Larry Heck
We present the first comprehensive evaluation of cross-lingual unlearning in multilingual LLMs. Using translated TOFU benchmarks in seven language/script variants, we test major un…