In Transformer models, the mechanism that allows the model to weigh the importance of each

element in the input sequence based on its context is called the Self-Attention Mechanism. This

mechanism is a key innovation of Transformer models, enabling them to process sequences of data,

such as natural language, by focusing on different parts of the sequence when making predictions1.

The Self-Attention Mechanism works by assigning a weight to each element in the input sequence,

indicating how much focus the model should put on other parts of the sequence when predicting a

particular element. This allows the model to consider the entire context of the sequence, which is

particularly useful for tasks that require an understanding of the relationships and dependencies

between words in a sentence or text sequence1.

Feedforward Neural Networks (Option OA) are a basic type of neural network where the connections

between nodes do not form a cycle and do not have an attention mechanism. Latent Space (Option

C) refers to the abstract representation space where input data is encoded. Random Seed (Option

OD) is a number used to initialize a pseudorandom number generator and is not related to the

attention mechanism in Transformer models. Therefore, the correct answer is B. Self-Attention

Mechanism, as it is the mechanism that enables Transformer models to learn contextual

relationships between elements in a sequence1.