3 citations · 4 across the 4 of their papers we have counts for
4 papers
Learning to Detect Noisy Labels Using Model-Based Features
Zhihao Wang, Zongyu Lin, Peiqi Liu +5
Label noise is ubiquitous in various machine learning scenarios such as self-labeling with model predictions and erroneous data annotation. Many existing approaches are based on he…
GPS: Genetic Prompt Search for Efficient Few-shot Learning
Hanwei Xu, Yujun Chen, Yulun Du +4
Prompt-based techniques have demostrated great potential for improving the few-shot generalization of pretrained language models. However, their performance heavily relies on the m…
ZeroPrompt: Scaling Prompt-Based Pretraining to 1,000 Tasks Improves Zero-Shot Generalization
Hanwei Xu, Yujun Chen, Yulun Du +4
We propose a multitask pretraining approach ZeroPrompt for zero-shot generalization, focusing on task scaling and zero-shot prompting. While previous models are trained on only a f…
Distribution Matching for Rationalization
Yongfeng Huang, Yujun Chen, Yulun Du +1
The task of rationalization aims to extract pieces of input text as rationales to justify neural network predictions on text classification tasks. By definition, rationales represe…