How does multi-head attention improve upon single self-attention within the Transformer architecture?

Multi-head attention enhances single self-attention by allowing the model to attend to different parts of the input sequence and capture different relationships between words simultaneously. Self-attention, at its core, calculates how much each word in a sequence relates to every other word, creating a weighted representation of the sequence. In single self-attention, this process happens only once, limiting the model to learning only one type of relationship.....