5 papers
CondPSE: A Polynomial-Filtered Structural Encoder with Conditional Modulation for Graphs
Woohyun Lee, Hogun Park
Message-passing graph neural networks are bounded by the 1-WL test and can miss topological structure that distinguishes non-isomorphic graphs. Positional and structural encodings…
Self-supervised Adversarial Purification for Graph Neural Networks
Woohyun Lee, Hogun Park
Defending Graph Neural Networks (GNNs) against adversarial attacks requires balancing accuracy and robustness, a trade-off often mishandled by traditional methods like adversarial…
MKG-RAG: Multi-hop Multimodal Knowledge Graph-enhanced Retrieval-Augmented Generation
Hyeongcheol Park, Jiyoung Seo, Jaewon Mun +6
Retrieval-Augmented Generation (RAG) has recently been extended to multimodal settings, connecting multimodal large language models (MLLMs) with vast corpora of external knowledge…
VAT-KG: Knowledge-Intensive Multimodal Knowledge Graph Dataset for Retrieval-Augmented Generation
Hyeongcheol Park, Jiyoung Seo, MinHyuk Jang +5
Multimodal Knowledge Graphs (MMKGs), which represent explicit knowledge across multiple modalities, play a pivotal role by complementing the implicit knowledge of Multimodal Large…
Large Language Models Are Better Logical Fallacy Reasoners with Counterargument, Explanation, and Goal-Aware Prompt Formulation
Jiwon Jeong, Hyeju Jang, Hogun Park
The advancement of Large Language Models (LLMs) has greatly improved our ability to process complex language. However, accurately detecting logical fallacies remains a significant…