3 accepted papers
The strategy of selecting ``most informative'' hard samples in active learning has proven a boon for alleviating the challenges of few-shot learning and costly data annotation in deep learning. However, this very preference towards hard samples engenders bias issues, thereby impeding the full potent…
While large language models (LLMs) have shown remarkable capabilities in natural language processing, they struggle with complex, multi-step reasoning tasks involving knowledge graphs (KGs). Existing approaches that integrate LLMs and KGs either underutilize the reasoning abilities of LLMs or suffer…
The task of entity alignment (EA) seeks to identify corresponding entities across different knowledge graphs (KGs). However, in large-scale KG alignment tasks, the complexity of the problem renders traditional entity structure representation methods, designed for small-scale KGs, ineffective. Partit…