What is the primary function of a Feed Forward Network in a Transformer model?

The primary function of a Feed Forward Network (FFN) within a Transformer model is indeed to capture complex relationships in input embeddings. In Transformers, after the attention mechanism processes the relationships between tokens, the Feed Forward Network applies non-linear transformations to the resulting representations. This aspect allows the model to learn intricate patterns and mappings from the embedded input data.

The Feed Forward Network consists of two linear transformations with a non-linear activation function in between, typically a ReLU. This structure is crucial for enabling the model to project input embeddings into different dimensional spaces, facilitating the learning of more abstract and richer representations.

The other options provided relate to different components and functionalities of the Transformer architecture. Generating token sequences falls under the model's overall capability, while normalizing attention scores typically involves layer normalization, a separate process. Encoding input tokens is primarily handled by the embedding layers and attention mechanisms, not solely the Feed Forward Network. Thus, recognizing the role of the FFN in the broader context of capturing complexities in input embeddings solidifies its importance within the architecture of a Transformer model.