How does the concept of Multi-Head Attention in Transformer Architecture enhance the capabilities of Deep Learning Models in the context of Transfer Learning?

Multi‑head attention lets a transformer look at several different relationships in the same data at once, each “head” focusing on a distinct pattern such as word order, word meaning, or long‑range dependencies. Because each head learns a separate view, the model captures a richer set of features than a single attention mechanism could. When a pretrained transformer is transferred to a new task, these diverse, already‑learned patterns can be reused, so the model needs fewer new parameters and trains faster. For example, a language model pretrained on books can be fine‑tuned for sentiment analysis, and the head that learned sentiment‑related word pairs can be reused almost unchanged. This ability to reuse multiple, complementary representations makes multi‑head attention especially powerful for transfer learning.