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…
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…
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…