24 citations · 24 across the 4 of their papers we have counts for
10 papers
Large Language Models Can Self-Improve
Jiaxin Huang, Shixiang Shane Gu, Le Hou +4
Large Language Models (LLMs) have achieved excellent performances in various tasks. However, fine-tuning an LLM requires extensive supervision. Human, on the other hand, may improv…
Unsupervised Reinforcement Adaptation for Class-Imbalanced Text Classification
Yuexin Wu, Xiaolei Huang
Class imbalance naturally exists when train and test models in different domains. Unsupervised domain adaptation (UDA) augments model performance with only accessible annotations f…
Token Dropping for Efficient BERT Pretraining
Le Hou, Richard Yuanzhe Pang, Tianyi Zhou +4
Transformer-based models generally allocate the same amount of computation for each token in a given sequence. We develop a simple but effective "token dropping" method to accelera…
TADO: Time-varying Attention with Dual-Optimizer Model
Yuexin Wu, Tianyu Gao, Sihao Wang +1
The review-based recommender systems are commonly utilized to measure users preferences towards different items. In this paper, we focus on addressing three main problems existing…
Knowledge Embedding Based Graph Convolutional Network
Donghan Yu, Yiming Yang, Ruohong Zhang +1
Recently, a considerable literature has grown up around the theme of Graph Convolutional Network (GCN). How to effectively leverage the rich structural information in complex graph…
Graph-Revised Convolutional Network
Donghan Yu, Ruohong Zhang, Zhengbao Jiang +2
Graph Convolutional Networks (GCNs) have received increasing attention in the machine learning community for effectively leveraging both the content features of nodes and the linka…