  This paper investigates the key role of Feed-Forward Networks (FFNs) in
transformer models by utilizing the Parallel Attention and Feed-Forward Net
Design (PAF) architecture, and comparing it to their Series Attention and
Feed-Forward Net Design (SAF) counterparts. Central to the effectiveness of PAF
are two main assumptions regarding the FFN block and the attention block within
a layer: 1) the primary function of the FFN block is to maintain isotropy among
token embeddings and prevent their degeneration, and 2) the residual norm
computed in the attention block is substantially smaller than the input token
embedding norm. To empirically validate these assumptions, we train PAF
variants of two large language models (RoBERTa-large and bert-large-uncased).
Our results demonstrate that both assumptions hold true in the PAF design. This
study contributes to a deeper understanding of the roles and interactions
between FFNs and self-attention mechanisms in transformer architectures.
