activity
20232026
most citedGraph Retrieval-Augmented Generation: A Survey

26 citations · 47 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CL2026

PILOT: Planning via Internalized Latent Optimization Trajectories for Large Language Models

Haoyu Zheng, Yun Zhu, Yuqian Yuan +4

Strategic planning is critical for multi-step reasoning, yet compact Large Language Models (LLMs) often lack the capacity to formulate global strategies, leading to error propagati…

cs.CL2024

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…

cs.LG2024

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…

cs.AI2024★ 26 cited

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…

cs.LG2024

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…

cs.CL2024

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…

cs.AI2023

Negative Sampling with Adaptive Denoising Mixup for Knowledge Graph Embedding

Xiangnan Chen, Wen Zhang, Zhen Yao +2

Knowledge graph embedding (KGE) aims to map entities and relations of a knowledge graph (KG) into a low-dimensional and dense vector space via contrasting the positive and negative…

cs.LG2023★ 21 cited

GraphControl: Adding Conditional Control to Universal Graph Pre-trained Models for Graph Domain Transfer Learning

Yun Zhu, Yaoke Wang, Haizhou Shi +3

Graph-structured data is ubiquitous in the world which models complex relationships between objects, enabling various Web applications. Daily influxes of unlabeled graph data on th…