Language Models are Few-Shot Learners

This paper introduces GPT-3, a large language model with 175 billion parameters. It demonstrates that scaling up language models significantly improves performance on various natural language tasks, even with only a few examples (few-shot learning). GPT-3 achieves strong results in tasks like translation, question-answering, and text generation, showcasing the potential of large language models to perform complex tasks with minimal task-specific training. Code generation, explanation, and improvement are some of the tasks that a large language model like GPT-3 can perform.