98 citations · 210 across the 8 of their papers we have counts for
8 papers
MergeME: Model Merging Techniques for Homogeneous and Heterogeneous MoEs
Yuhang Zhou, Giannis Karamanolakis, Victor Soto +5
The recent success of specialized Large Language Models (LLMs) in domains such as mathematical reasoning and coding has led to growing interest in methods for merging these expert…
Ensuring Safety and Trust: Analyzing the Risks of Large Language Models in Medicine
Yifan Yang, Qiao Jin, Robert Leaman +15
The remarkable capabilities of Large Language Models (LLMs) make them increasingly compelling for adoption in real-world healthcare applications. However, the risks associated with…
CSRec: Rethinking Sequential Recommendation from A Causal Perspective
Xiaoyu Liu, Jiaxin Yuan, Yuhang Zhou +3
The essence of sequential recommender systems (RecSys) lies in understanding how users make decisions. Most existing approaches frame the task as sequential prediction based on use…
Adversarial Attacks on Large Language Models in Medicine
Yifan Yang, Qiao Jin, Furong Huang +1
The integration of Large Language Models (LLMs) into healthcare applications offers promising advancements in medical diagnostics, treatment recommendations, and patient care. Howe…
Large Language Models and Causal Inference in Collaboration: A Survey
Xiaoyu Liu, Paiheng Xu, Junda Wu +10
Causal inference has shown potential in enhancing the predictive accuracy, fairness, robustness, and explainability of Natural Language Processing (NLP) models by capturing causal…
A survey of recent methods for addressing AI fairness and bias in biomedicine
Yifan Yang, Mingquan Lin, Han Zhao +3
Artificial intelligence (AI) systems have the potential to revolutionize clinical practices, including improving diagnostic accuracy and surgical decision-making, while also reduci…
Unmasking and Quantifying Racial Bias of Large Language Models in Medical Report Generation
Yifan Yang, Xiaoyu Liu, Qiao Jin +2
Large language models like GPT-3.5-turbo and GPT-4 hold promise for healthcare professionals, but they may inadvertently inherit biases during their training, potentially affecting…
C-Disentanglement: Discovering Causally-Independent Generative Factors under an Inductive Bias of Confounder
Xiaoyu Liu, Jiaxin Yuan, Bang An +3
Representation learning assumes that real-world data is generated by a few semantically meaningful generative factors (i.e., sources of variation) and aims to discover them in the…