1.4k citations · 1.7k across the 32 of their papers we have counts for
3 papers · 2 filters
MoEfication: Transformer Feed-forward Layers are Mixtures of Experts
Zhengyan Zhang, Yankai Lin, Zhiyuan Liu +3
Recent work has shown that feed-forward networks (FFNs) in pre-trained Transformers are a key component, storing various linguistic and factual knowledge. However, the computationa…
CSS-LM: A Contrastive Framework for Semi-supervised Fine-tuning of Pre-trained Language Models
Yusheng Su, Xu Han, Yankai Lin +5
Fine-tuning pre-trained language models (PLMs) has demonstrated its effectiveness on various downstream NLP tasks recently. However, in many low-resource scenarios, the conventiona…
Red Alarm for Pre-trained Models: Universal Vulnerability to Neuron-Level Backdoor Attacks
Zhengyan Zhang, Guangxuan Xiao, Yongwei Li +6
Pre-trained models (PTMs) have been widely used in various downstream tasks. The parameters of PTMs are distributed on the Internet and may suffer backdoor attacks. In this work, w…