Transfer learning in natural language processing (NLP) involves training a model on a data-rich task before fine-tuning it for a specific task. A new study introduces a unified framework that converts all text-based language problems into a text-to-text format. By comparing different pre-training objectives, architectures, and data sets, the researchers achieved top results on various language tasks like summarization and question answering. They also released their data set, pre-trained models, and code to help future research in this area.