6 papers
DAG-MoE: From Simple Mixture to Structural Aggregation in Mixture-of-Experts
Jiarui Feng, Hanqing Zeng, Karish Grover +11
Mixture-of-Experts (MoE) models have become a leading approach for decoupling parameter count from computational cost in large language models, yet effectively scaling MoE performa…
TabDLM: Free-Form Tabular Data Generation via Joint Numerical-Language Diffusion
Donghong Cai, Jiarui Feng, Yanbo Wang +3
Synthetic tabular data generation has attracted growing attention due to its importance for data augmentation, foundation models, and privacy. However, real-world tabular datasets…
GRIP: In-Parameter Graph Reasoning through Fine-Tuning Large Language Models
Jiarui Feng, Donghong Cai, Yixin Chen +1
Large Language Models (LLMs) have demonstrated remarkable capabilities in modeling sequential textual data and generalizing across diverse tasks. However, effectively adapting LLMs…
Addressing accuracy and hallucination of LLMs in Alzheimer's disease research through knowledge graphs
Tingxuan Xu, Jiarui Feng, Justin Melendez +6
In the past two years, large language model (LLM)-based chatbots, such as ChatGPT, have revolutionized various domains by enabling diverse task completion and question-answering ca…
GOFA: A Generative One-For-All Model for Joint Graph Language Modeling
Lecheng Kong, Jiarui Feng, Hao Liu +4
Foundation models, such as Large Language Models (LLMs) or Large Vision Models (LVMs), have emerged as one of the most powerful tools in the respective fields. However, unlike text…
Large Language Models Meet Graph Neural Networks for Text-Numeric Graph Reasoning
Haoran Song, Jiarui Feng, Guangfu Li +4
In real-world scientific discovery, human beings always make use of the accumulated prior knowledge with imagination pick select one or a few most promising hypotheses from large a…