This paper presents a novel approach based on variable forgetting, which is a useful tool in resolving contradictory by filtering some given variables, to merging multiple knowledge bases. This paper first builds a relationship between belief merging and variable forgetting by using dilation. Variable forgetting is applied to capture belief merging operation. Finally, some new merging operators are developed by modifying candidate variables to amend the shortage of traditional merging operators. Different from model selection of traditional merging operators, as an alternative approach, variable selection in those new operators could provide intuitive information about an atom variable among whole knowledge bases.
Countering Catastrophic Forgetting of Large Language Models for Better Instruction Following via Weight-Space Model Merging
University of Florida（佛罗里达大学）；Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida（佛罗里达大学医学院健康结果与生物医学信息学系）；Department of Engineering Education, Herbert Wertheim College of Engineering, University of Florida（佛罗里达大学赫伯特·韦特海姆工程学院工程教育系）；Preston A. Wells, Jr. Center for Brain Tumor Therapy, Lillian S. Wells Department of Neurosurgery, University of Florida（佛罗里达大学莉莉安·S·威尔斯神经外科系普雷斯顿·A·威尔斯脑肿瘤治疗中心）
RECALL: REpresentation-aligned Catastrophic-forgetting ALLeviation via Hierarchical Model Merging
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Peng Cheng Laboratory（鹏城实验室）；Huazhong University of Science and Technology（华中科技大学）；Xiamen University（厦门大学）；The Hong Kong University of Science and Technology, Guangzhou（香港科技大学（广州））；School of Biomedical Engineering, Tsinghua University（清华大学生物医学工程学院）
Merging without Forgetting: Continual Fusion of Task-Specific Models via Optimal Transport
Tsinghua University（清华大学）；University of Oxford（牛津大学）；East China Normal University（华东师范大学）；Zhejiang University（浙江大学）；Tencent（腾讯）；Southern University of Science and Technology（南方科技大学）
MagMax: Leveraging Model Merging for Seamless Continual Learning
IDEAS NCBR；Warsaw University of Technology（华沙工业大学）；Autonomous University of Barcelona（巴塞罗那自治大学）；Computer Vision Center（计算机视觉中心）；Tooploox；Gdańsk University of Technology（格但斯克工业大学）
Personalized Forgetting Mechanism with Concept-Driven Knowledge Tracing
Anhui University（安徽大学）；University of Science and Technology of China（中国科学技术大学）；Sichuan University（四川大学）；Zhejiang Dahua Technology Co., Ltd.（浙江大华技术股份有限公司）
A Novel Hierarchical Integration Method for Efficient Model Merging in Medical LLMs
Deakin Cyber Research（德肯网络研究）；Innovation Centre Deakin University / Universal Higher Education Melbourne, Australia（德肯大学创新中心/通用高等教育墨尔本，澳大利亚）；School of IT（信息科技学院）；Engineering Melbourne Institute of Technology Melbourne, Australia（工程墨尔本技术学院墨尔本，澳大利亚）；Innovation Centre Deakin University Geelong, Australia（德肯大学创新中心吉朗，澳大利亚）
MemoryKT: An Integrative Memory-and-Forgetting Method for Knowledge Tracing
School of Computer Science, South China Normal University, Guangzhou, China（华南师范大学计算机科学学院）；School of Computing, National University of Singapore, Singapore（新加坡国立大学计算学院）；Sichuan Artificial Intelligence Research Institute, Sichuan, China（四川省人工智能研究院）
