Explain the significance of transfer learning in AI, particularly in the context of pre-trained language models like BERT and GPT. How do these models benefit various downstream NLP tasks?

Transfer learning leverages pre-trained models on large datasets to improve performance on related downstream tasks with limited data. Pre-trained language models like BERT and GPT capture rich contextual representations of language by being trained on vast amounts of text data. These models can be fine-tuned for specific tasks such as sentiment analysis, question answering, and named entity recognition, significantly boosting performance and reducing the need for extensive task-specific training data.