5 papers
MKG-RAG-Bench: Benchmarking Retrieval in Multimodal Knowledge Graph-Augmented Generation
Xiaochen Wang, Bao Hoang, Han Liu +2
Retrieval-augmented generation (RAG) over knowledge graphs has emerged as a promising approach for grounding large language models, yet existing benchmarks largely overlook the cha…
SEP-Attack: A Simple and Effective Paradigm for Transfer-Based Textual Adversarial Attack
Han Liu, Zhi Xu, Xiaotong Zhang +5
Despite the strong performance of deep neural networks in modern Web and language applications, they remain vulnerable to adversarial attacks, especially transferable attacks that…
HQA-VLAttack: Towards High Quality Adversarial Attack on Vision-Language Pre-Trained Models
Han Liu, Jiaqi Li, Zhi Xu +5
Black-box adversarial attack on vision-language pre-trained models is a practical and challenging task, as text and image perturbations need to be considered simultaneously, and on…
Towards Collaborative Fairness in Federated Learning Under Imbalanced Covariate Shift
Tianrun Yu, Jiaqi Wang, Haoyu Wang +4
Collaborative fairness is a crucial challenge in federated learning. However, existing approaches often overlook a practical yet complex form of heterogeneity: imbalanced covariate…
Liberating Seen Classes: Boosting Few-Shot and Zero-Shot Text Classification via Anchor Generation and Classification Reframing
Han Liu, Siyang Zhao, Xiaotong Zhang +6
Few-shot and zero-shot text classification aim to recognize samples from novel classes with limited labeled samples or no labeled samples at all. While prevailing methods have show…