What is a key advantage of using transfer learning in Natural Language Processing?

The key advantage of using transfer learning in Natural Language Processing (NLP) is that it allows models to leverage knowledge from previous models. In practice, transfer learning enables the application of a model that has been trained on a large and diverse dataset to be fine-tuned for a specific task with much less data. This is particularly beneficial in NLP because high-quality labeled data can be scarce and expensive to obtain for every possible task. By utilizing pre-trained models, which have already learned general language patterns and structures, developers can adapt these models to new tasks or domains more efficiently. This ability to build upon the knowledge acquired from prior training dramatically reduces the time and resources needed to develop effective NLP solutions.