Transformers are deep learning models that use self-attention mechanisms in an encoder-decoder feed-forward neural network. They can process sequential data the same way that RNNs do.
Self-attention
Transformers don’t use hidden states to capture the interdependencies of data sequences. Instead, they use a self-attention head to process data sequences in parallel. This enables transformers to train and process longer sequences in less time than an RNN does. With the self-attention mechanism, transformers overcome the memory limitations and sequence interdependencies that RNNs face. Transformers can process data sequences in parallel and use positional encoding to remember how each input relates to others.
Parallelism
Transformers solve the gradient issues that RNNs face by enabling parallelism during training. By processing all input sequences simultaneously, a transformer isn’t subjected to backpropagation restrictions because gradients can flow freely to all weights. They are also optimized for parallel computing, which graphic processing units (GPUs) offer for generative AI developments. Parallelism enables transformers to scale massively and handle complex NLP tasks by building larger models.