10 citations · 14 across the 11 of their papers we have counts for
12 papers
CUFG: Curriculum Unlearning Guided by the Forgetting Gradient
Jiaxing Miao, Liang Hu, Qi Zhang +2
As privacy and security take center stage in AI, machine unlearning, the ability to erase specific knowledge from models, has garnered increasing attention. However, existing metho…
COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement
Zhihao Sui, Liang Hu, Jian Cao +3
Large deep learning models have achieved significant success in various tasks. However, the performance of a model can significantly degrade if it is needed to train on datasets wi…
Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy
Zhihao Sui, Liang Hu, Jian Cao +4
Machine Unlearning (MU) technology facilitates the removal of the influence of specific data instances from trained models on request. Despite rapid advancements in MU technology,…
LLMs on a Budget? Say HOLA
Zohaib Hasan Siddiqui, Jiechao Gao, Ebad Shabbir +4
Running Large Language Models (LLMs) on edge devices is constrained by high compute and memory demands posing a barrier for real-time applications in sectors like healthcare, educa…
XGUARD: A Graded Benchmark for Evaluating Safety Failures of Large Language Models on Extremist Content
Vadivel Abishethvarman, Bhavik Chandna, Pratik Jalan +1
Large Language Models (LLMs) can generate content spanning ideological rhetoric to explicit instructions for violence. However, existing safety evaluations often rely on simplistic…
Multimodal Generative AI with Autoregressive LLMs for Human Motion Understanding and Generation: A Way Forward
Muhammad Islam, Tao Huang, Euijoon Ahn +1
This paper presents an in-depth survey on the use of multimodal Generative Artificial Intelligence (GenAI) and autoregressive Large Language Models (LLMs) for human motion understa…
A Survey on Progress in LLM Alignment from the Perspective of Reward Design
Miaomiao Ji, Yanqiu Wu, Zhibin Wu +4
Reward design plays a pivotal role in aligning large language models (LLMs) with human values, serving as the bridge between feedback signals and model optimization. This survey pr…
COSEE: Consistency-Oriented Signal-Based Early Exiting via Calibrated Sample Weighting Mechanism
Jianing He, Qi Zhang, Hongyun Zhang +3
Early exiting is an effective paradigm for improving the inference efficiency of pre-trained language models (PLMs) by dynamically adjusting the number of executed layers for each…
Structural Representation Learning and Disentanglement for Evidential Chinese Patent Approval Prediction
Jinzhi Shan, Qi Zhang, Chongyang Shi +3
Automatic Chinese patent approval prediction is an emerging and valuable task in patent analysis. However, it involves a rigorous and transparent decision-making process that inclu…
Wills Aligner: Multi-Subject Collaborative Brain Visual Decoding
Guangyin Bao, Qi Zhang, Zixuan Gong +6
Decoding visual information from human brain activity has seen remarkable advancements in recent research. However, the diversity in cortical parcellation and fMRI patterns across…
Causal Intervention for Abstractive Related Work Generation
Jiachang Liu, Qi Zhang, Chongyang Shi +3
Abstractive related work generation has attracted increasing attention in generating coherent related work that better helps readers grasp the background in the current research. H…
Rumor Detection with Hierarchical Representation on Bipartite Adhoc Event Trees
Qi Zhang, Yayi Yang, Chongyang Shi +4
The rapid growth of social media has caused tremendous effects on information propagation, raising extreme challenges in detecting rumors. Existing rumor detection methods typicall…