Activation Editing, which involves directly editting the internal representations of large language models (LLMs) to alter their behaviors and achieve desired properties, has emerged as a promising area of research. Existing works primarily treat LLMs' activations as points in space and modify them by adding steering vectors. However, this approach is limited in its ability to achieve greater performance improvement while maintaining the necessary consistency of activation magnitudes. To overcome these issues, we propose a novel editing method that views activations in terms of their directions and magnitudes. Our method, named Householder Pseudo-Rotation (HPR), mimics the rotation transformation, thus preserving activation norms and resulting in an improved performance on various safety benchmarks.
Output Vector Editing for Memorization Mitigation in Large Language Models
Center for Information and Language Processing, LMU Munich（慕尼黑大学语言与信息处理中心）；Department of Computer Science, University of Copenhagen（哥本哈根大学计算机科学系）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Pioneer Centre for AI（人工智能先锋中心）
DPN-LE: Dual Personality Neuron Localization and Editing for Large Language Models
Southeast University（东南大学）；Shanghai Jiao Tong University（上海交通大学）；East China Normal University（华东师范大学）；Zhongguancun Academy（中关村学院）；Zhejiang University of Technology（浙江工业大学）；Kuaishou Technology（快手科技）；Northeastern University（东北大学）；Tsinghua University（清华大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）
Beyond the Covariance Trap: Unlocking Generalization in Same-Subject Knowledge Editing for Large Language Models
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络安全学院）
ACE: Attribution-Controlled Knowledge Editing for Multi-hop Factual Recall
HKUST(GZ) Deep Interdisciplinary Intelligence Lab（香港科技大学(广州)深 interdisciplinary 智能实验室）；BUAA（北京航空航天大学）；HKUST(GZ) Institute of Deep Perception Technology, JITRI Deep Interdisciplinary Intelligence Lab（香港科技大学(广州)深度感知技术研究所，JITRI 深 interdisciplinary 智能实验室）
Keys to Robust Edits: from Theoretical Insights to Practical Advances
Zhejiang University（浙江大学）；School of Engineering, Westlake University（西湖大学工程学院）；Institute of Advanced Technology, Westlake Institute for Advanced Study（西湖先进研究院技术研究所）；Shanghai AI Lab（上海人工智能实验室）
Compiling Activation Steering into Weights via Null-Space Constraints for Stealthy Backdoors
Zhejiang University（浙江大学）；Palo Alto Networks（帕洛阿尔托网络）；Ningbo Global Innovation Center, Zhejiang University（宁波全球创新中心，浙江大学）；OPPO Research Institute（OPPO研究院）；Qilu University of Technology（齐鲁大学）
Activation-Guided Local Editing for Jailbreaking Attacks
Beihang University（北航）；Hangzhou Innovation Institute（杭州创新研究院）；HKUST（香港科技大学）；South China University of Technology（华南理工大学）；Nanyang Technological University（南洋理工大学）；Kunming University of Science and Technology（昆明理工大学）
Latent Adversarial Training Improves Robustness to Persistent Harmful Behaviors in LLMs
Georgia Institute of Technology（佐治亚理工学院）；University of Bristol（布里斯托大学）；University of Maryland（马里兰大学）；University College London（伦敦大学学院）；MATS；Astra；MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）
