5 papers
Unlocking the Potentials of Retrieval-Augmented Generation for Diffusion Language Models
Chuanyue Yu, Jiahui Wang, Yuhan Li +6
Diffusion Language Models (DLMs) have recently demonstrated remarkable capabilities in natural language processing tasks. However, the potential of Retrieval-Augmented Generation (…
GraphRAG-R1: Graph Retrieval-Augmented Generation with Process-Constrained Reinforcement Learning
Chuanyue Yu, Kuo Zhao, Yuhan Li +8
Graph Retrieval-Augmented Generation (GraphRAG) has shown great effectiveness in enhancing the reasoning abilities of LLMs by leveraging graph structures for knowledge representati…
Revisiting LoRA through the Lens of Parameter Redundancy: Spectral Encoding Helps
Jiashun Cheng, Aochuan Chen, Nuo Chen +4
Low-Rank Adaptation (LoRA) has emerged as a prominent technique for fine-tuning large foundation models. Despite its successes, the substantial parameter redundancy, which limits t…
Can LLMs Alleviate Catastrophic Forgetting in Graph Continual Learning? A Systematic Study
Ziyang Cheng, Zhixun Li, Yuhan Li +6
Nowadays, real-world data, including graph-structure data, often arrives in a streaming manner, which means that learning systems need to continuously acquire new knowledge without…
G-Refer: Graph Retrieval-Augmented Large Language Model for Explainable Recommendation
Yuhan Li, Xinni Zhang, Linhao Luo +4
Explainable recommendation has demonstrated significant advantages in informing users about the logic behind recommendations, thereby increasing system transparency, effectiveness,…