AI Technology & Industry Review
A team from University of Massachusetts Amherst and Google Research proposes STraTA, an approach that combines task augmentation and self-training to leverage unlabelled data and improve sample efficiency and performance on NLP tasks.
A research team from Baidu proposes ERNIE 3.0, a unified framework for pretraining large-scale, knowledge-enhanced models that can easily be tailored for both natural language understanding and generation tasks with zero-shot learning, few-shot learning or fine-tuning, and achieves state-of-the-art results on NLP tasks.
A research team from New York University, Facebook AI, and a CIFAR Fellow in Learning in Machines & Brains raise doubts regarding large-scale pretrained language models’ few-shot learning abilities. The researchers re-evaluate such abilities with held-out examples unavailable, which they propose constitutes “true few-shot learning.”
A new simple baseline for few-shot learning that achieves state-of-the-art performance.
Google AI researchers introduce Meta-Dataset, a large-scale and diverse benchmark for measuring the ability of few-shot classification models.
In fact, the accuracy of results in few-shot learning, both with and without labeled data, is very high.