Language Models are Few-Shot Learners

This paper introduces GPT-3, a large-scale language model with 175 billion parameters. It demonstrates that GPT-3 can achieve strong performance on various natural language tasks with few-shot learning, meaning it can generate coherent text, translate languages, answer questions, and perform other tasks with only a few examples, without the need for fine-tuning. The research suggests that large language models can significantly improve performance and flexibility in NLP.