In the context of Transformer architecture, how does self-attention enhance the process of transfer learning?

Self‑attention lets every token look at every other token in a sentence, so the model learns rich, context‑aware representations that capture long‑range dependencies. When a Transformer is pretrained on a huge corpus, these attention patterns encode general language knowledge, which can be reused for new tasks. During fine‑tuning, the same attention weights can be adapted with only a few extra training steps, because the model already knows how to combine information from distant tokens. For example, a language model pretrained with self‑attention can be fine‑tuned to classify sentiment with minimal data, because the attention layers already understand how words relate across the whole text. Thus, self‑attention provides a flexible, reusable feature extractor that speeds up and improves transfer learning.