Post-training makes LLMs over-confident; we recalibrate them without any labels by aligning their confidence to the still well-calibrated pre-trained model, using only the examples on which the two models agree.
a.k.a. Bale · PhD student
College of Computing and Data Science (CCDS), Nanyang Technological University
Hi, I'm Beier. My Chinese name 贝尔 is pronounced like “Bale”, so I also go by Bale. With my surname Luo in front, my name sounds like “Nobel”, which I take as a good omen for my research career.
I am a first-year PhD student at the College of Computing and Data Science (CCDS), Nanyang Technological University, supervised by Dr. Sean Du. Previously, I spent two wonderful years as a graduate student in the group of Dr. Hongxin Wei at Southern University of Science and Technology (SUSTech), where I also earned my Bachelor's degree in Statistics.
My research focuses on building reliable and trustworthy large language models. I am broadly interested in understanding, modeling, and improving the confidence and uncertainty behavior of modern machine learning systems, particularly under distribution shift and open-ended generation.
Current directions
† denotes equal contribution
I'm always excited to explore new research directions and open to collaborating. If you're interested in my research or just want to chat, reach me at LUOB0007@e.ntu.edu.sg.