Language Models are Unsupervised Multitask Learners
This paper introduces GPT-2, a 1.5-billion-parameter Transformer language model trained on a large web-text corpus (WebText) with a simple next-token prediction objective. It demonstrates that a sufficiently large language model can perform many NLP tasks in a zero-shot setting, without task-specific training data or fine-tuning. The work argued that unsupervised language modeling at scale implicitly learns to perform downstream tasks from naturally occurring demonstrations.