Multi-head attention improves self-attention by allowing the model to attend to information from different representation subspaces simultaneously. Instead of computing a single attention score, it splits inputs into multiple heads, each learning unique aspects of the sequence. These are then concatenated and combined, offering a richer understanding of the input. In the article, multi-head attention helps illustrate how Transformers can focus on different kinds of relationships in parallel. This design enhances flexibility and performance. It's a central innovation that makes self-attention more expressive. Recognizing its role is key to grasping Transformer effectiveness.