2 citations · 2 across the 10 of their papers we have counts for
10 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…
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…
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…
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…
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…
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…
Do We Really Need Graph Convolution During Training? Light Post-Training Graph-ODE for Efficient Recommendation
Weizhi Zhang, Liangwei Yang, Zihe Song +4
The efficiency and scalability of graph convolution networks (GCNs) in training recommender systems (RecSys) have been persistent concerns, hindering their deployment in real-world…
Mixed Supervised Graph Contrastive Learning for Recommendation
Weizhi Zhang, Liangwei Yang, Zihe Song +4
Recommender systems (RecSys) play a vital role in online platforms, offering users personalized suggestions amidst vast information. Graph contrastive learning aims to learn from h…
ImplicitAVE: An Open-Source Dataset and Multimodal LLMs Benchmark for Implicit Attribute Value Extraction
Henry Peng Zou, Vinay Samuel, Yue Zhou +5
Existing datasets for attribute value extraction (AVE) predominantly focus on explicit attribute values while neglecting the implicit ones, lack product images, are often not publi…
Cyclic Neural Network
Liangwei Yang, Hengrui Zhang, Zihe Song +4
This paper answers a fundamental question in artificial neural network (ANN) design: We do not need to build ANNs layer-by-layer sequentially to guarantee the Directed Acyclic Grap…