We demonstrate that scaling up language models greatly improves task-agnostic, few-shot
performance, sometimes even becoming competitive with prior state-of-the-art fine-tuning …

When scaled to hundreds of billions of parameters, pretrained language models such as
GPT-3 (Brown et al., 2020) achieve remarkable few-shot performance. However, enormous …

The recent GPT-3 model (Brown et al., 2020) achieves remarkable few-shot performance
solely by leveraging a natural-language prompt and a few task demonstrations as input …

Large language models have been shown to achieve remarkable performance across a
variety of natural language tasks using few-shot learning, which drastically reduces the …

GPT-3 can perform numerous tasks when provided a natural language prompt that contains
a few training examples. We show that this type of few-shot learning can be unstable: the …

As unlabeled data carry rich task-relevant information, they are proven useful for few-shot
learning of language model. The question is how to effectively make use of such data. In this …

Current methods for few-shot fine-tuning of pretrained masked language models (PLMs)
require carefully engineered prompts and verbalizers for each new task to convert examples …

Large-scale generative language models such as GPT-3 are competitive few-shot learners.
While these models are known to be able to jointly represent many different languages, their …

Few-shot natural language processing (NLP) refers to NLP tasks that are accompanied with
merely a handful of labeled examples. This is a real-world challenge that an AI system must …

Large language models have shown impressive few-shot results on a wide range of tasks.
However, when knowledge is key for such results, as is the case for tasks such as question …