13 citations · 15 across the 7 of their papers we have counts for
6 papers · 1 filter
HV-Attack: Hierarchical Visual Attack for Multimodal Retrieval Augmented Generation
Linyin Luo, Yujuan Ding, Yunshan Ma +2
Advanced multimodal Retrieval-Augmented Generation (MRAG) techniques have been widely applied to enhance the capabilities of Large Multimodal Models (LMMs), but they also bring alo…
Rethinking Graph Domain Adaptation: A Spectral Contrastive Perspective
Haoyu Zhang, Yuxuan Cheng, Wenqi Fan +2
Graph neural networks (GNNs) have achieved remarkable success in various domains, yet they often struggle with domain adaptation due to significant structural distribution shifts a…
Towards Graph Foundation Models: A Transferability Perspective
Yuxiang Wang, Wenqi Fan, Suhang Wang +1
In recent years, Graph Foundation Models (GFMs) have gained significant attention for their potential to generalize across diverse graph domains and tasks. Some works focus on Doma…
Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey
Bo Ni, Zheyuan Liu, Leyao Wang +17
Retrieval-Augmented Generation (RAG) is an advanced technique designed to address the challenges of Artificial Intelligence-Generated Content (AIGC). By integrating context retriev…
Exploring Graph Learning Tasks with Pure LLMs: A Comprehensive Benchmark and Investigation
Yuxiang Wang, Xinnan Dai, Wenqi Fan +1
In recent years, large language models (LLMs) have emerged as promising candidates for graph tasks. Many studies leverage natural language to describe graphs and apply LLMs for rea…
Uni-Retrieval: A Multi-Style Retrieval Framework for STEM's Education
Yanhao Jia, Xinyi Wu, Hao Li +4
In AI-facilitated teaching, leveraging various query styles to interpret abstract text descriptions is crucial for ensuring high-quality teaching. However, current retrieval models…