Hugging Face Trending Papers

Mark, Don't Erase: Token Inoculation for Dual-Use Knowledge in LLMs

Safety interventions on dual-use knowledge typically choose between destroying hazardous content (e. g.

arXiv AI
Jun 29

CBD: API-Only LLM Black-Box Unlearning through Controlled Behavioral Divergence

arXiv:2606. 27683v1 Announce Type: cross Abstract: Edge devices increasingly invoke large language models (LLMs) through API services for context aware edge intelligence, while edge generated data may be collected to improve LLMs and may introduce sensitive, copyrighted, harmful, or outdated information into model behavior.

By Zhiqiang Xie, Yijing Lin, Zhipeng Gao, Dong In Kim
arXiv AI
Jul 3

DemoPSD: Disagreement-Modulated Policy Self-Distillation

arXiv:2607. 02502v1 Announce Type: cross Abstract: On-policy self-distillation (OPSD) has emerged as a practical method for training large language models (LLMs) to reason, where a single model acts as both the teacher and the student with different levels of information access.

By Yunhe Li, Hao Shi, Wenhao Liu, Mengzhe Ruan, Hanxu Hou, Zhongxiang Dai, Shuang Qiu, Linqi Song
arXiv Machine Learning
Aug 11

When Skills Meet Safety: Benchmarking and Characterizing the Adaptive Jailbreak Robustness of Skill-Merged LLMs

arXiv:2608. 08542v1 Announce Type: new Abstract: Model merging has become the default way to give an aligned language model new skills without retraining: a practitioner folds task vectors from math, code, or domain specialists into a safety-aligned base using task arithmetic, TIES, or DARE.

By Yu Ma, Hongli Shi, Jing Li, Xinran Xu, Weiwei Hou