Knowledge editing aims at updating knowledge of large language models (LLMs) to prevent them from becoming outdated. Existing work edits LLMs at the level of factual knowledge triplets. However, natural knowledge updates in the real world come from the occurrences of new events rather than direct changes in factual triplets. In this paper, we propose a new task setting: event-level knowledge editing, which directly edits new events into LLMs and improves over conventional triplet-level editing on (1) Efficiency. A single event edit leads to updates in multiple entailed knowledge triplets. (2) Completeness. Beyond updating factual knowledge, event-level editing also requires considering the event influences and updating LLMs' knowledge about future trends. We construct a high-quality event-level editing benchmark ELKEN, consisting of 1,515 event edits, 6,449 questions about factual knowledge, and 10,150 questions about future tendencies. We systematically evaluate the performance of various knowledge editing methods and LLMs on this benchmark. We find that ELKEN poses significant challenges to existing knowledge editing approaches. Our codes and dataset are publicly released to facilitate further research.
Related Knowledge Perturbation Matters: Rethinking Multiple Pieces of Knowledge Editing in Same-Subject
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；People’s Public Security University of China（中国人民公安大学）
Orthogonal Representation Editing: Decoupling Semantic Entanglement in Batch Knowledge Editing of LLMs
School of Computer Science and Technology, Tianjin University（天津大学计算机科学与技术学院）；Kexin Technology（可心科技）；University of Chinese Academy of Sciences（中国科学院大学）；Tencent Hunyuan（腾讯混元）；College of Computer Science, Chongqing University（重庆大学计算机学院）
Assessing Knowledge Editing in Language Models via Relation Perspective
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Automation, CAS（中国科学院自动化研究所）；Kunming University of Science and Technology（昆明理工大学）；Beijing Institute of Technology（北京理工大学）
MILA - Quebec AI Institute（MILA - 魁北克人工智能研究所）；Microsoft（微软公司）；UNC Chapel Hill（北卡罗来纳大学教堂山分校）；North Carolina State University（北卡罗来纳州立大学）；Massachusetts Institute of Technology（麻省理工学院）；Google DeepMind（谷歌DeepMind）
Commonsense Knowledge Editing Based on Free-Text in LLMs
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Beijing Academy of Artificial Intelligence（北京智源人工智能研究院）
Editing Factual Knowledge and Explanatory Ability of Medical Large Language Models
University of Science and Technology of China（中国科学技术大学）；City University of Hong Kong（香港城市大学）；Jarvis Research Center（贾维斯研究中心）；Tencent YouTu Lab（腾讯优图实验室）；Peking University（北京大学）
StruEdit: Structured Outputs Enable the Fast and Accurate Knowledge Editing for Large Language Models
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；University of Science and Technology of China（中国科学技术大学）
Knowledge Updating? No More Model Editing! Just Selective Contextual Reasoning
School of Economics and Management, East China Normal University（华东师范大学经济与管理学部）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
Context-Robust Knowledge Editing for Language Models
Graduate School of Data Science, Seoul National University（首尔国立大学数据科学研究生院）；School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology（全州科学技术院电子工程与计算机科学学院）
Spectral Characterization and Mitigation of Sequential Knowledge Editing Collapse
Shandong University（山东大学）；School of Computer Science, Beijing University of Posts and Telecommunications（北京邮电大学计算机学院）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）
Knowledge in Superposition: Unveiling the Failures of Lifelong Knowledge Editing for Large Language Models
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
The Labyrinth and the Thread: Rethinking Regularizations in Sequential Knowledge Editing for Large Language Models
Bosch Center for Artificial Intelligence (BCAI)（博世人工智能中心（BCAI））；Bosch (China) Investment Ltd.（博世（中国）投资有限公司）；School of Statistics, East China Normal University（东华大学统计学院）
BMIKE-53: Investigating Cross-Lingual Knowledge Editing with In-Context Learning
LMU Munich（慕尼黑大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Technical University of Munich（慕尼黑技术大学）；Bosch Center for Artificial Intelligence（博世人工智能中心）；University of Oxford（牛津大学）；CISPA Helmholtz Center for Information Security（CISPA海德堡信息安全中心）
Adaptive Token Biaser: Knowledge Editing via Biasing Key Entities
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
Knowledge Graph Enhanced Large Language Model Editing
School of Computer Science and Technology, Shandong University（山东大学计算机科学与技术学院）；School of Computer Science, Beijing University of Posts and Telecommunications（北京邮电大学计算机学院）
REACT: Representation Extraction And Controllable Tuning to Overcome Overfitting in LLM Knowledge Editing
NLPR, MAIS, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所国家信息处理实验室、人工智能研究所）；Cuiying Honors College, Lanzhou University（兰州大学萃英荣誉学院）；Department of Mathematics, The Chinese University of Hong Kong（香港中文大学数学系）；Tencent（腾讯）
MMKE-Bench: A Multimodal Editing Benchmark for Diverse Visual Knowledge
BIGAI（北京智源人工智能研究院）；Shandong University（山东大学）；University of Science and Technology of China（中国科学技术大学）；Peking University（北京大学）；Beijing Institute of Technology（北京理工大学）
Knowledge Editing for Large Language Model with Knowledge Neuronal Ensemble
School of Computer Science and Engineering, Northeastern University（东北大学计算机科学与工程学院）；Artificial Intelligence Innovation Center, Yangtze Delta Region Institute of Tsinghua University, Zhejiang（浙江清华长三角研究院人工智能创新中心）；Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University（东北大学医学影像智能计算教育部重点实验室）
History Matters: Temporal Knowledge Editing in Large Language Model
Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机研究所）；Center for Data Science, Peking University（北京大学数据科学中心）；School of Law, Tsinghua University（清华大学法学院）
VLKEB: A Large Vision-Language Model Knowledge Editing Benchmark
University of Chinese Academy of Sciences (UCAS)（中国科学院大学）；Institute of Automation, Chinese Academy of Sciences (CASIA)（中国科学院自动化研究所）；Nanjing University（南京大学）
Beyond Memorization: A Rigorous Evaluation Framework for Medical Knowledge Editing
School of Information and Communication Technology, Griffith University（格里菲斯大学信息与通信技术学院）；Department of Data Science and AI, Monash University（莫纳什大学数据科学与人工智能系）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Department of Computer Science, Emory University（埃默里大学计算机科学系）
