198 citations · 580 across the 17 of their papers we have counts for
6 papers · 1 filter
Finetuned Language Models Are Zero-Shot Learners
Jason Wei, Maarten Bosma, Vincent Y. Zhao +6
This paper explores a simple method for improving the zero-shot learning abilities of language models. We show that instruction tuning -- finetuning language models on a collection…
The MultiBERTs: BERT Reproductions for Robustness Analysis
Thibault Sellam, Steve Yadlowsky, Jason Wei +9
Experiments with pre-trained models such as BERT are often based on a single checkpoint. While the conclusions drawn apply to the artifact tested in the experiment (i.e., the parti…
A Survey of Data Augmentation Approaches for NLP
Steven Y. Feng, Varun Gangal, Jason Wei +4
Data augmentation has recently seen increased interest in NLP due to more work in low-resource domains, new tasks, and the popularity of large-scale neural networks that require la…
Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learning
Jason Wei, Chengyu Huang, Soroush Vosoughi +2
Few-shot text classification is a fundamental NLP task in which a model aims to classify text into a large number of categories, given only a few training examples per category. Th…
Text Augmentation in a Multi-Task View
Jason Wei, Chengyu Huang, Shiqi Xu +1
Traditional data augmentation aims to increase the coverage of the input distribution by generating augmented examples that strongly resemble original samples in an online fashion…
A Petri Dish for Histopathology Image Analysis
Jerry Wei, Arief Suriawinata, Bing Ren +9
With the rise of deep learning, there has been increased interest in using neural networks for histopathology image analysis, a field that investigates the properties of biopsy or…