26 citations · 47 across the 8 of their papers we have counts for
5 papers · 1 filter
Meta-Reflection: A Feedback-Free Reflection Learning Framework
Yaoke Wang, Yun Zhu, Xintong Bao +7
Despite the remarkable capabilities of large language models (LLMs) in natural language understanding and reasoning, they often display undesirable behaviors, such as generating ha…
GraphCLIP: Enhancing Transferability in Graph Foundation Models for Text-Attributed Graphs
Yun Zhu, Haizhou Shi, Xiaotang Wang +5
Recently, research on Text-Attributed Graphs (TAGs) has gained significant attention due to the prevalence of free-text node features in real-world applications and the advancement…
Graph Retrieval-Augmented Generation: A Survey
Boci Peng, Yun Zhu, Yongchao Liu +5
Recently, Retrieval-Augmented Generation (RAG) has achieved remarkable success in addressing the challenges of Large Language Models (LLMs) without necessitating retraining. By ref…
E-CGL: An Efficient Continual Graph Learner
Jianhao Guo, Zixuan Ni, Yun Zhu +1
Continual learning has emerged as a crucial paradigm for learning from sequential data while preserving previous knowledge. In the realm of continual graph learning, where graphs c…
Efficient Tuning and Inference for Large Language Models on Textual Graphs
Yun Zhu, Yaoke Wang, Haizhou Shi +1
Rich textual and topological information of textual graphs need to be modeled in real-world applications such as webpages, e-commerce, and academic articles. Practitioners have bee…