5 papers · 1 filter
Knowledge Homophily in Large Language Models
Utkarsh Sahu, Zhisheng Qi, Mahantesh Halappanavar +6
Large Language Models (LLMs) have been increasingly studied as neural knowledge bases for supporting knowledge-intensive applications such as question answering and fact checking.…
Forecasting Time Series with LLMs via Patch-Based Prompting and Decomposition
Mayank Bumb, Anshul Vemulapalli, Sri Harsha Vardhan Prasad Jella +7
Recent advances in Large Language Models (LLMs) have demonstrated new possibilities for accurate and efficient time series analysis, but prior work often required heavy fine-tuning…
Mixture of Structural-and-Textual Retrieval over Text-rich Graph Knowledge Bases
Yongjia Lei, Haoyu Han, Ryan A. Rossi +5
Text-rich Graph Knowledge Bases (TG-KBs) have become increasingly crucial for answering queries by providing textual and structural knowledge. However, current retrieval methods of…
Efficient Model Selection for Time Series Forecasting via LLMs
Wang Wei, Tiankai Yang, Hongjie Chen +4
Model selection is a critical step in time series forecasting, traditionally requiring extensive performance evaluations across various datasets. Meta-learning approaches aim to au…
Large Generative Graph Models
Yu Wang, Ryan A. Rossi, Namyong Park +6
Large Generative Models (LGMs) such as GPT, Stable Diffusion, Sora, and Suno are trained on a huge amount of language corpus, images, videos, and audio that are extremely diverse f…