3 citations · 13 across the 17 of their papers we have counts for
6 papers · 1 filter
SEFD: Semantic-Enhanced Framework for Detecting LLM-Generated Text
Weiqing He, Bojian Hou, Tianqi Shang +3
The widespread adoption of large language models (LLMs) has created an urgent need for robust tools to detect LLM-generated text, especially in light of \textit{paraphrasing} techn…
Leveraging Social Determinants of Health in Alzheimer's Research Using LLM-Augmented Literature Mining and Knowledge Graphs
Tianqi Shang, Shu Yang, Weiqing He +7
Growing evidence suggests that social determinants of health (SDoH), a set of nonmedical factors, affect individuals' risks of developing Alzheimer's disease (AD) and related demen…
Knowledge-Driven Feature Selection and Engineering for Genotype Data with Large Language Models
Joseph Lee, Shu Yang, Jae Young Baik +8
Predicting phenotypes with complex genetic bases based on a small, interpretable set of variant features remains a challenging task. Conventionally, data-driven approaches are util…
Fairness-Aware Estimation of Graphical Models
Zhuoping Zhou, Davoud Ataee Tarzanagh, Bojian Hou +2
This paper examines the issue of fairness in the estimation of graphical models (GMs), particularly Gaussian, Covariance, and Ising models. These models play a vital role in unders…
Fine-Tuning Attention Modules Only: Enhancing Weight Disentanglement in Task Arithmetic
Ruochen Jin, Bojian Hou, Jiancong Xiao +2
In recent years, task arithmetic has garnered increasing attention. This approach edits pre-trained models directly in weight space by combining the fine-tuned weights of various t…
DALK: Dynamic Co-Augmentation of LLMs and KG to answer Alzheimer's Disease Questions with Scientific Literature
Dawei Li, Shu Yang, Zhen Tan +10
Recent advancements in large language models (LLMs) have achieved promising performances across various applications. Nonetheless, the ongoing challenge of integrating long-tail kn…