9 citations · 9 across the 6 of their papers we have counts for
10 papers
ReMath: Benchmarking Theorem Retrieval in Research-Level Mathematics
Zicheng Lyu, Wenjie Yang, Shengzhong Zhang +1
Large language models are increasingly capable at closed-world mathematical reasoning, but research assistance also requires source-grounded use of the literature. When a proof rea…
Rethinking Multi-Label Node Classification: Do Tuned Classic GNNs Suffice?
Yuxuan Xiao, Shengzhong Zhang
Multi-label node classification (MLNC) has recently been addressed by increasingly complex label-aware designs that explicitly model node-label interactions and inter-label depende…
Right Is Not Enough: The Pitfalls of Outcome Supervision in Training LLMs for Math Reasoning
Jiaxing Guo, Wenjie Yang, Shengzhong Zhang +4
Outcome-rewarded Large Language Models (LLMs) have demonstrated remarkable success in mathematical problem-solving. However, this success often masks a critical issue: models frequ…
Your Graph Recommender is Provably a Single-view Graph Contrastive Learning
Wenjie Yang, Shengzhong Zhang, Jiaxing Guo +1
Graph recommender (GR) is a type of graph neural network (GNNs) encoder that is customized for extracting information from the user-item interaction graph. Due to its strong perfor…
Understanding Community Bias Amplification in Graph Representation Learning
Shengzhong Zhang, Wenjie Yang, Yimin Zhang +3
In this work, we discover a phenomenon of community bias amplification in graph representation learning, which refers to the exacerbation of performance bias between different clas…
StructComp: Substituting Propagation with Structural Compression in Training Graph Contrastive Learning
Shengzhong Zhang, Wenjie Yang, Xinyuan Cao +2
Graph contrastive learning (GCL) has become a powerful tool for learning graph data, but its scalability remains a significant challenge. In this work, we propose a simple yet effe…