6 papers
BlockServe: Block-Grained Continuous Batching for High-Throughput Diffusion LLM Serving
Yuanjie Zhu, Liangwei Yang, Ke Xu +4
Efficient serving of diffusion large language models (dLLMs) is hindered by convergence heterogeneity: when batching multiple requests, different sequences converge at different ra…
LLM-MemCluster: Empowering Large Language Models with Dynamic Memory for Text Clustering
Yuanjie Zhu, Liangwei Yang, Ke Xu +4
Large Language Models (LLMs) are reshaping unsupervised learning by offering an unprecedented ability to perform text clustering based on their deep semantic understanding. However…
Detecting Hallucinations in Graph Retrieval-Augmented Generation via Attention Patterns and Semantic Alignment
Shanghao Li, Jinda Han, Yibo Wang +5
Graph-based Retrieval-Augmented Generation (GraphRAG) enhances Large Language Models (LLMs) by incorporating external knowledge from linearized subgraphs retrieved from knowledge g…
SGCL: Unifying Self-Supervised and Supervised Learning for Graph Recommendation
Weizhi Zhang, Liangwei Yang, Zihe Song +4
Recommender systems (RecSys) are essential for online platforms, providing personalized suggestions to users within a vast sea of information. Self-supervised graph learning seeks…
Item Cluster-aware Prompt Learning for Session-based Recommendation
Wooseong Yang, Chen Wang, Zihe Song +2
Session-based recommendation (SBR) aims to capture dynamic user preferences by analyzing item sequences within individual sessions. However, most existing approaches focus mainly o…
Graph Neural Controlled Differential Equations For Collaborative Filtering
Ke Xu, Weizhi Zhang, Zihe Song +2
Graph Convolution Networks (GCNs) are widely considered state-of-the-art for recommendation systems. Several studies in the field of recommendation systems have attempted to apply…