RealFormer is a new type of Transformer-based language model that uses residual attention to improve its performance. It is capable of creating multiple direct paths, each for a different type of attention module, without adding any parameters or hyper-parameters to the existing architecture.
What is a Transformer-based model?
A Transformer is a type of neural network architecture that is used for natural language processing tasks, such as language translation and text classification. It was introduced by Vaswani et al. in their 2017 paper "Attention is All You Need".
The main idea behind transformers is to replace the recurrent neural network (RNN) architecture, which is commonly used for sequence prediction tasks, with self-attention mechanisms. This allows the model to process a sequence of inputs in parallel, rather than one at a time, resulting in a significant improvement in efficiency and speed.
Transformers consist of an encoder and a decoder, each of which is made up of a stack of identical layers. Each layer has two sub-layers: a multi-head self-attention mechanism, and a position-wise fully connected feed forward network. Both of these sub-layers are connected by residual connections and layer normalization.
What is residual attention?
In a traditional transformer-based model, each layer has a single attention module that processes the input sequence. This can result in poor performance, as the model may not be able to identify all relevant information in the input sequence.
RealFormer seeks to address this issue by adding skip edges to the backbone Transformer, creating multiple direct paths, each for a different type of attention module. This allows the model to identify more relevant information in the input sequence, leading to improved performance.
What are skip edges?
Skip edges are connections between layers that allow information to flow directly from one layer to another, without passing through the intermediate layers. They are commonly used in residual neural networks, where they help to address the issue of vanishing gradients, which can occur when training deep neural networks.
In RealFormer, skip edges are added to the backbone Transformer to create multiple direct paths, one for each type of attention module. This allows the model to identify more relevant information in the input sequence, leading to improved performance.
How does RealFormer work?
RealFormer uses a post-LN style Transformer as its backbone, which means that layer normalization is applied after the multi-head attention and feed-forward sub-layers, instead of before. This has been shown to improve performance in other Transformer-based models.
RealFormer also adds skip edges to connect the multi-head attention modules in adjacent layers, creating multiple direct paths for information to flow through. Each direct path is associated with a different type of attention module, such as self-attention or cross-attention, allowing the model to identify more relevant information in the input sequence.
Overall, RealFormer seeks to improve the performance of Transformer-based models by using residual attention and skip edges to create multiple direct paths for information to flow through.
RealFormer is a new type of Transformer-based model that uses residual attention and skip edges to improve its performance. It is capable of creating multiple direct paths, each for a different type of attention module, without adding any parameters or hyper-parameters to the existing architecture. RealFormer has the potential to improve the state-of-the-art in natural language processing tasks, such as language translation and text classification.
