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publications
Your Pre-trained LLM is Secretly an Unsupervised Confidence Calibrator
Published in The Thirty-ninth Annual Conference on Neural Information Processing Systems , 2025
In this work, we propose a noval unsupervised calibration method to mitigate the over-confidence problem of LLMs introduced by post-training techniques. With the observation of the inherent well-calibrated nature of Pre-trained LMs (PLMs), we propose to leverage the output of PLMs on unlabeled data for post-hoc calibration.
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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