8 citations · 10 across the 5 of their papers we have counts for
5 papers
Benign Samples Matter! Fine-tuning On Outlier Benign Samples Severely Breaks Safety
Zihan Guan, Mengxuan Hu, Ronghang Zhu +2
Recent studies have uncovered a troubling vulnerability in the fine-tuning stage of large language models (LLMs): even fine-tuning on entirely benign datasets can lead to a signifi…
No Free Lunch: Retrieval-Augmented Generation Undermines Fairness in LLMs, Even for Vigilant Users
Mengxuan Hu, Hongyi Wu, Zihan Guan +4
Retrieval-Augmented Generation (RAG) is widely adopted for its effectiveness and cost-efficiency in mitigating hallucinations and enhancing the domain-specific generation capabilit…
Trustworthy Representation Learning Across Domains
Ronghang Zhu, Dongliang Guo, Daiqing Qi +3
As AI systems have obtained significant performance to be deployed widely in our daily live and human society, people both enjoy the benefits brought by these technologies and suff…
Co-embedding of Nodes and Edges with Graph Neural Networks
Xiaodong Jiang, Ronghang Zhu, Pengsheng Ji +1
Graph, as an important data representation, is ubiquitous in many real world applications ranging from social network analysis to biology. How to correctly and effectively learn an…
Disentangling Features in 3D Face Shapes for Joint Face Reconstruction and Recognition
Feng Liu, Ronghang Zhu, Dan Zeng +2
This paper proposes an encoder-decoder network to disentangle shape features during 3D face reconstruction from single 2D images, such that the tasks of reconstructing accurate 3D…