From the 1 of 18 linked papers with an AI index.
18 papers
Sharpness-aware Model Merging with Salience Recovery for LLM-based Cross-Domain Sequential Recommendation
Huwei Ji, Jiajie Su, Yuyuan Li +2
The paper introduces SharpRec, a method that merges large language models for cross-domain sequential recommendation by using sharpness-aware geometric alignment and preference sal…
Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks
Nobin Sarwar, Shubhashis Roy Dipta, Zheyuan Liu +1
With the growing adoption of VLMs, DMs, LLMs, and AFMs, these multimodal foundation models can inadvertently encode sensitive, copyrighted, biased, or unsafe cross-modal associatio…
Demystifying the Optimal Fair Classifier in Multi-Class Classification
Li Zhang, Yuyuan Li, XiaoHua Feng +3
Ensuring fair and equitable treatment across diverse groups, particularly in multi-class classification tasks, poses a significant challenge due to the persistent biases inherent i…
Taming the Long Tail: Efficient Item-wise Sharpness-Aware Minimization for LLM-based Recommender Systems
Jiaming Zhang, Yuyuan Li, Xiaohua Feng +4
Large Language Model-based Recommender Systems (LRSs) have recently emerged as a new paradigm in sequential recommendation by directly adopting LLMs as backbones. While LRSs demons…
Sharpness-Aware Minimization for Generalized Embedding Learning in Federated Recommendation
Fengyuan Yu, Xiaohua Feng, Yuyuan Li +3
Federated recommender systems enable collaborative model training while keeping user interaction data local and sharing only essential model parameters, thereby mitigating privacy…
Generalizable Multimodal Large Language Model Editing via Invariant Trajectory Learning
Jiajie Su, Haoyuan Wang, Xiaohua Feng +6
Knowledge editing emerges as a crucial technique for efficiently correcting incorrect or outdated knowledge in large language models (LLM). Existing editing methods rely on a rigid…