How many weight matrices are needed for multi-headed self-attention?

The correct answer focuses on the structure of multi-headed self-attention. In this mechanism, each attention head operates independently and is responsible for learning different representations of the input data. Therefore, each attention head needs its own set of weight matrices.

In the context of multi-headed self-attention, a single weight matrix is not sufficient because each head must have distinct weights to capture varied aspects of the input. Typically, if there are, for example, five heads, there will be five separate weight matrices—one for each head that accommodates the unique way that head interprets relationships within the data.

In contrast to the other options, the structure of multi-headed self-attention does not just combine different heads into one cohesive weight matrix, nor does it rely solely on input embeddings that would reduce the complexity necessary for capturing diverse feature representations across several types of information. Each head's independent weight matrix is fundamental for the model to learn effectively from the nuances present in the input sequences.