9 citations · 11 across the 8 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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)…
Time Matters: Examine Temporal Effects on Biomedical Language Models
Weisi Liu, Zhe He, Xiaolei Huang
Time roots in applying language models for biomedical applications: models are trained on historical data and will be deployed for new or future data, which may vary from training…
Chain-of-Interaction: Enhancing Large Language Models for Psychiatric Behavior Understanding by Dyadic Contexts
Guangzeng Han, Weisi Liu, Xiaolei Huang +1
Automatic coding patient behaviors is essential to support decision making for psychotherapists during the motivational interviewing (MI), a collaborative communication interventio…