4 papers
LLM-based Agents for Automated Confounder Discovery and Subgroup Analysis in Causal Inference
Po-Han Lee, Yu-Cheng Lin, Chan-Tung Ku +4
Estimating individualized treatment effects from observational data presents a persistent challenge due to unmeasured confounding and structural bias. Causal Machine Learning (caus…
Towards Simulating Social Influence Dynamics with LLM-based Multi-agents
Hsien-Tsung Lin, Pei-Cing Huang, Chan-Tung Ku +3
Recent advancements in Large Language Models offer promising capabilities to simulate complex human social interactions. We investigate whether LLM-based multi-agent simulations ca…
Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework
Peng-Yi Wu, Pei-Cing Huang, Ting-Yu Chen +3
Accurate and interpretable prediction of estimated glomerular filtration rate (eGFR) is essential for managing chronic kidney disease (CKD) and supporting clinical decisions. Recen…
CodEv: An Automated Grading Framework Leveraging Large Language Models for Consistent and Constructive Feedback
En-Qi Tseng, Pei-Cing Huang, Chan Hsu +3
Grading programming assignments is crucial for guiding students to improve their programming skills and coding styles. This study presents an automated grading framework, CodEv, wh…