Summarizing the Paper on the Transformer Architecture
Main Ideas:
- The paper explores the role of the Feed Forward Network (FFN) component in the Transformer architecture.
- Attention and the FFN are the two main non-embedding components of the Transformer architecture.
- While attention captures interdependencies between words, the FFN non-linearly transforms each input token independently.
- The researchers find that the FFN, despite its significant share of parameters, is highly redundant.
- By reducing the number of FFN parameters, the model’s overall parameter count can be decreased without a major loss in accuracy.
Author’s Take:
The paper delves into the role of the Feed Forward Network (FFN) within the Transformer architecture. By discovering that the FFN is highly redundant despite its parameter volume, the researchers present an opportunity to significantly reduce the number of parameters in the model without sacrificing accuracy. This finding offers important insights into the optimization of the Transformer architecture and its potential for more efficient and streamlined implementations.