10 citations · 22 across the 11 of their papers we have counts for
5 papers · 1 filter
R-Tuning: Instructing Large Language Models to Say `I Don't Know'
Hanning Zhang, Shizhe Diao, Yong Lin +6
Large language models (LLMs) have revolutionized numerous domains with their impressive performance but still face their challenges. A predominant issue is the propensity for these…
Continuous Invariance Learning
Yong Lin, Fan Zhou, Lu Tan +8
Invariance learning methods aim to learn invariant features in the hope that they generalize under distributional shifts. Although many tasks are naturally characterized by continu…
Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning
Yong Lin, Chen Liu, Chenlu Ye +3
Modern deep learning heavily relies on large labeled datasets, which often comse with high costs in terms of both manual labeling and computational resources. To mitigate these cha…
Spurious Feature Diversification Improves Out-of-distribution Generalization
Yong Lin, Lu Tan, Yifan Hao +5
Generalization to out-of-distribution (OOD) data is a critical challenge in machine learning. Ensemble-based methods, like weight space ensembles that interpolate model parameters,…
Mitigating the Alignment Tax of RLHF
Yong Lin, Hangyu Lin, Wei Xiong +14
LLMs acquire a wide range of abilities during pre-training, but aligning LLMs under Reinforcement Learning with Human Feedback (RLHF) can lead to forgetting pretrained abilities, w…