This 2020 paper outlines the development and evaluation of GPT-3, a large language model, exploring its performance across various natural language processing tasks under zero-shot, one-shot, and few-shot learning conditions, which involve providing minimal to no task-specific examples during inference. It details the model's architecture, training methodology, including its use of a massive dataset, and analyzes its limitations and broader impacts, such as the potential for misuse and the presence of biases related to gender, race, and religion inherited from its training data. The document also discusses the challenges of data contamination and the computational resources required for training such a large model.