4 papers
SePO: Self-Evolving Prompt Agent for System Prompt Optimization
Wangcheng Tao, Han Wu, Weng-Fai Wong
System prompt optimization improves agent behavior without modifying the underlying model, yielding human-readable, model-agnostic instructions. Existing methods build a prompt age…
MoEMeta: Mixture-of-Experts Meta Learning for Few-Shot Relational Learning
Han Wu, Jie Yin
Few-shot knowledge graph relational learning seeks to perform reasoning over relations given only a limited number of training examples. While existing approaches largely adopt a m…
Meta-Semantics Augmented Few-Shot Relational Learning
Han Wu, Jie Yin
Few-shot relational learning on knowledge graph (KGs) aims to perform reasoning over relations with only a few training examples. While current methods have focused primarily on le…
Unbiased Online Curvature Approximation for Regularized Graph Continual Learning
Jie Yin, Ke Sun, Han Wu
Graph continual learning (GCL) aims to learn from a continuous sequence of graph-based tasks. Regularization methods are vital for preventing catastrophic forgetting in GCL, partic…