3 citations · 10 across the 19 of their papers we have counts for
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Knowledge Acquisition During Pre-training? Large Language Models Learn Better With Auxiliary Views
Joseph Lee, Yidi Huang, Dokyoon Kim +2
Gaps remain in our understanding of how large language models (LLMs) acquire knowledge during pre-training. We find that auxiliary views, conceptual reformulations, are key to this…
iLENS: Interpretable LLM-Guided Mixture-of-Experts for Neuroimaging Survival Analysis
Farica Zhuang, Seong Woo Han, Zixuan Wen +3
Alzheimer's Disease (AD) is a complex neurodegenerative disorder that continues to impact millions of people worldwide. Predicting AD conversion during the prodromal stage remains…
A Semantic-Sampling Framework for Evaluating Calibration in Open-Ended Question Answering
Zhanliang Wang, Jiancong Xiao, Ruochen Jin +3
Calibration measures whether a model's predicted confidence aligns with its empirical accuracy, and is central to the reliable deployment of large language models (LLMs) in high-st…
Tabular LLMs for Interpretable Few-Shot Alzheimer's Disease Prediction with Multimodal Biomedical Data
Sophie Kearney, Shu Yang, Zixuan Wen +8
Accurate diagnosis of Alzheimer's disease (AD) requires handling tabular biomarker data, yet such data are often small and incomplete, where deep learning models frequently fail to…