The authors of the GPT-1 paper demonstrated that substantial improvements in various natural language processing tasks, such as textual entailment, question answering, and semantic similarity assessment, can be achieved through a two-stage process. First, they employed generative pre-training on a large and diverse corpus of unlabeled text to create a robust language model. Following this, they fine-tuned the model discriminatively on each specific task, allowing it to adapt its general language understanding to the nuances of particular tasks, resulting in significant performance gains.