3 accepted papers
Despite the rapid progress of Large Vision-Language Models (LVLMs), the integration of visual modalities introduces new safety vulnerabilities that adversaries can exploit to elicit biased or malicious outputs. In this paper, we demonstrate an underexplored vulnerability via semantic slot filling, w
In knowledge graphs (KGs), predicting missing relations is a critical reasoning task. Recent subgraph-based models have delved into inductive settings, which aim to predict relations between newly added entities. While these models have demonstrated the ability for inductive reasoning, they only con…
Temporal evolution attribute graph prediction, a key task in graph machine learning, aims to forecast the dynamic evolution of node attributes over time. While recent advances in Large Language Models (LLMs) have enabled their use in enhancing node representations for integration with Graph Neural N