3 citations · 7 across the 16 of their papers we have counts for
6 papers · 1 filter
Do LLMs Really Forget? Hidden-State Leakage in Model Unlearning and How to Fix it
Hadi Reisizadeh, Jiajun Ruan, Sijia Liu +1
Unlearning in large language models (LLMs) is typically evaluated at the output level, where a model appears to suppress sensitive or undesirable content. In this work, we show tha…
Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks
Nobin Sarwar, Shubhashis Roy Dipta, Zheyuan Liu +1
With the growing adoption of VLMs, DMs, LLMs, and AFMs, these multimodal foundation models can inadvertently encode sensitive, copyrighted, biased, or unsafe cross-modal associatio…
See What I See, Know What I Think: Dense Latent Communication Across Heterogeneous Agents
Siyi Chen, Xiaoyan Zhang, Meng Wu +7
Language-model agents build internal representations of the information they observe and the reasoning they perform. Sharing these representations offers a way to communicate both…
PrivUn: Unveiling Latent Ripple Effects and Shallow Forgetting in Privacy Unlearning
Xiaoyi Chen, Haoyuan Wang, Siyuan Tang +4
Large language models (LLMs) often memorize private information during training, raising serious privacy concerns. While machine unlearning has emerged as a promising solution, its…
Attention Smoothing Is All You Need For Unlearning
Saleh Zare Zade, Xiangyu Zhou, Sijia Liu +1
Large Language Models are prone to memorizing sensitive, copyrighted, or hazardous content, posing significant privacy and legal concerns. Retraining from scratch is computationall…
Powering Up Zeroth-Order Training via Subspace Gradient Orthogonalization
Yicheng Lang, Changsheng Wang, Yihua Zhang +4
Zeroth-order (ZO) optimization provides a gradient-free alternative to first-order (FO) methods by estimating gradients via finite differences of function evaluations, and has rece…