However, the paper lacks insight into why the Brainformer architecture is so effective and only evaluates it on a limited set of tasks and datasets. The authors suggest that the diversity of layer types gives the model more expressive power, but do not delve deeper into the underlying reasons. Further research is needed to fully understand the reasons behind the Brainformer's success and explore its broader applicability. Despite these limitations, the paper makes an important contribution in demonstrating the potential benefits of more complex Transformer block designs.
Key takeaways:
- The paper explores the potential of more complex Transformer block designs, specifically a new block called the Brainformer, which includes a variety of layers like sparse feed-forward, dense feed-forward, attention, and different normalization and activation functions.
- The Brainformer consistently outperforms state-of-the-art dense and sparse Transformer models in terms of both quality of results and computational efficiency.
- A Brainformer model with 8 billion parameters trains 2x faster and runs 5x faster per step compared to a similar-sized GLaM Transformer, and also achieved a 3% higher score on a benchmark language understanding task.
- The paper suggests that more flexible and diverse Transformer architectures, like the Brainformer, can lead to significant performance improvements over the standard Transformer design.