8 papers
TimeRoute: Time-Aware Modality Routing and Diffusion for Multi-Modal Recommendation
Pengyu Zhang, Yangqin Jiang, Klim Zaporojets +2
Multi-modal recommenders fuse user-item interaction signals with item modalities such as text, images, and audio, but the usefulness of each drifts over time and at different rates…
Select, Don't Train: The Benefits of Modular Entity Disambiguation with LLM-Based Selection
Fina Polat, Daniel Daza, Pengyu Zhang +2
Entity Disambiguation (ED) is a key task for constructing and using knowledge graphs. State-of-the-art neural approaches commonly model ED as a single task, although it consists of…
Fusion Training for Mathematical Generalization in Large Language Models
Congfeng Cao, Pengyu Zhang, Jelke Bloem
Thinking Mode Fusion (TMF) enables large language models to support both concise responses and long-form reasoning by unifying a non-thinking mode and a thinking mode within a sing…
Time Imprint: Learning Time-Aware Representations in Multi-Modal Knowledge Graphs
Pengyu Zhang, Klim Zaporojets, Congfeng Cao +2
Multi-Modal Knowledge Graphs (MMKGs) enrich entities with multiple modalities such as text and images, yet entities with highly similar multi-modal features remain difficult to dis…
Are a Thousand Words Better Than a Single Picture? Beyond Images -- A Framework for Multi-Modal Knowledge Graph Dataset Enrichment
Pengyu Zhang, Klim Zaporojets, Jie Liu +2
Multi-Modal Knowledge Graphs (MMKGs) benefit from visual information, yet large-scale image collection is hard to curate and often excludes ambiguous but relevant visuals (e.g., lo…
A Survey of Large Language Models for Data Challenges in Graphs
Mengran Li, Pengyu Zhang, Wenbin Xing +11
Graphs are a widely used paradigm for representing non-Euclidean data, with applications ranging from social network analysis to biomolecular prediction. While graph learning has a…
TIGER: Temporally Improved Graph Entity Linker
Pengyu Zhang, Congfeng Cao, Paul Groth
Knowledge graphs change over time, for example, when new entities are introduced or entity descriptions change. This impacts the performance of entity linking, a key task in many u…
CYCLE: Cross-Year Contrastive Learning in Entity-Linking
Pengyu Zhang, Congfeng Cao, Klim Zaporojets +1
Knowledge graphs constantly evolve with new entities emerging, existing definitions being revised, and entity relationships changing. These changes lead to temporal degradation in…