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 …

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 …

Recent studies have revealed the intriguing few-shot learning ability of pretrained language
models (PLMs): They can quickly adapt to a new task when fine-tuned on a small amount of …

Prompting language models (LMs) with training examples and task descriptions has been
seen as critical to recent successes in few-shot learning. In this work, we show that …

Large pre-trained language models (LMs) have demonstrated remarkable ability as few-shot
learners. However, their success hinges largely on scaling model parameters to a degree …

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 …

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 …

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 …

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 …

Large pre-trained language models have shown promise for few-shot learning, completing
text-based tasks given only a few task-specific examples. Will models soon solve …