As Large Langue Models have been shown to memorize real-world facts, the need to update this knowledge in a controlled and efficient manner arises. Designed with these constraints in mind, Knowledge Editing (KE) approaches propose to alter specific facts in pretrained models. However, they have been shown to suffer from several limitations, including their lack of contextual robustness and their failure to generalize to logical implications related to the fact. To overcome these issues, we propose SAKE, a steering activation method that models a fact to be edited as a distribution rather than a single prompt. Leveraging Optimal Transport, SAKE alters the LLM behavior over a whole fact-related distribution, defined as paraphrases and logical implications. Several numerical experiments demonstrate the effectiveness of this method: SAKE is thus able to perform more robust edits than its existing counterparts.
SLIM: Sparse Latent Steering for Interpretable and Property-Directed LLM-Based Molecular Editing
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；The 63rd Research Institute, National University of Defense Technology, Nanjing（国防科技大学第六三研究所，南京）
DRESSing Up LLM: Efficient Stylized Question-Answering via Style Subspace Editing
School of Computer Science, Peking University（北京大学计算机学院）；Center on Frontiers of Computing Studies, Peking University（北京大学计算前沿研究中心）；National Research and Engineering Center of Software Engineering, Peking University（北京大学国家软件工程研究与工程中心）
Is Model Editing Built on Sand? Revealing Its Illusory Success and Fragile Foundation
National University of Singapore（新加坡国立大学）；Huazhong University of Science and Technology（华中科技大学）；Central China Normal University（中国地质大学）；iWudao Tech（iWudao科技）；Oxford（牛津大学）；Google Deepmind（谷歌DeepMind）
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）
Detoxifying Large Language Models via Autoregressive Reward Guided Representation Editing
SKLCCSE, Beihang University（北航智能计算与复杂系统研究院）；National University of Singapore（新加坡国立大学）；Zhongguancun Laboratory, Beijing（中关村实验室）；Institute of Dataspace, Hefei（合肥数据空间研究院）；Nanyang Technological University（南洋理工大学）
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）
