3 citations · 13 across the 17 of their papers we have counts for
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Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration
Ruochen Jin, Zhanliang Wang, Zongyu Dai +2
Preference alignment often makes large language models (LLMs) overconfident and poorly calibrated. Traditional post-hoc temperature scaling is inherently domain-dependent: a temper…
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…