Dec 22, 2023 · In the context of Transformers, the Feedforward operation is a key component that takes the output of the self-attention mechanism and transforms it into the final output of the model.

Sep 3, 2020 · feed-forward layers in transformerbased language models operate as key-value memories, where each key correlates with textual patterns in the training examples, and each value induces a distribution over the output vocabulary.

Dec 29, 2020 · We show that feed-forward layers in transformer-based language models operate as key-value memories, where each key correlates with textual patterns in the training examples, and each value induces a distribution over the output vocabulary.

May 24, 2024 · Now, the second step is the feed-forward neural network. A simple feed-forward neural network is applied to every attention vector to transform the attention vectors into a form that is acceptable to the next encoder or decoder layer.

Apr 19, 2024 · The Transformer model revolutionizes language processing with its unique architecture, which includes a crucial component known as the Feedforward Network (FFN).

The insights gained from studying feed-forward layers can inform the development of interpretability methods, help address concerns related to training-data privacy, and guide the design of novel architectures that overcome the limitations of current transformer models.

Mar 3, 2024 · Feed-forward layers constitute two-thirds of the parameters in a transformer model, yet their specific function has been relatively under-explored compared to the self-attention layers....

3 days ago · We show that feed-forward layers in transformer-based language models operate as key-value memories, where each key correlates with textual patterns in the training examples, and each value induces a distribution over the output vocabulary.

May 22, 2023 · 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 …

We show that feed-forward layers in transformer-based language models operate as key-value memories, where each key correlates with tex-tual patterns in the training examples, and each value induces a distribution over the output vocabulary.