6 papers
Knowledge-driven Augmentation and Retrieval for Integrative Temporal Adaptation
Weisi Liu, Guangzeng Han, Xiaolei Huang
Time introduces fundamental challenges in model development and deployment: models are usually trained on historical data while deployed on future data where semantic distributions…
Cultivating Multidisciplinary AI Workforce Development on iTiger GPU Cluster: Practices and Challenges
Mayira Sharif, Guangzeng Han, Weisi Liu +1
To support rapid AI advances and broaden access to large-scale computing resources for under-resourced institutions at the Mid-South, we established the first regional mid-scale GP…
A Scoping Review of Synthetic Data Generation by Language Models in Biomedical Research and Application: Data Utility and Quality Perspectives
Hanshu Rao, Weisi Liu, Haohan Wang +3
Synthetic data generation using large language models (LLMs) demonstrates substantial promise in addressing biomedical data challenges and shows increasing adoption in biomedical r…
Attributes as Textual Genes: Leveraging LLMs as Genetic Algorithm Simulators for Conditional Synthetic Data Generation
Guangzeng Han, Weisi Liu, Xiaolei Huang
Large Language Models (LLMs) excel at generating synthetic data, but ensuring its quality and diversity remains challenging. We propose Genetic Prompt, a novel framework that combi…
Examining Imbalance Effects on Performance and Demographic Fairness of Clinical Language Models
Precious Jones, Weisi Liu, I-Chan Huang +1
Data imbalance is a fundamental challenge in applying language models to biomedical applications, particularly in ICD code prediction tasks where label and demographic distribution…
Examining and Adapting Time for Multilingual Classification via Mixture of Temporal Experts
Weisi Liu, Guangzeng Han, Xiaolei Huang
Time is implicitly embedded in classification process: classifiers are usually built on existing data while to be applied on future data whose distributions (e.g., label and token)…