The transformer architecture is a significant improvement over traditional neural network architectures for large language models. Here are a few ways in which it has improved the field :

Parallelization : Traditional neural network architectures, such as recurrent neural networks (RNNs), process sequential input one element at a time, making them computationally expensive and slow. The transformer architecture, on the other hand, can process the entire sequence in parallel, making it much more efficient and scalable.

Self-Attention : The transformer architecture is based on a self-attention mechanism, which allows the model to weigh different parts of the input sequence differently based on their relevance to the output. This allows the model to focus on the most important parts of the input sequence and ignore irrelevant information, leading to more accurate predictions.

Long-term Dependencies : The self-attention mechanism in the transformer architecture is particularly effective for handling long-term dependencies in sequential data, such as natural language. This is because it allows the model to capture dependencies between distant parts of the sequence, something that is difficult for traditional RNNs to do.

Transfer Learning : The transformer architecture has proven to be highly effective for transfer learning, which is the process of pre-training a model on a large dataset and then fine-tuning it on a smaller task-specific dataset. This allows the model to learn general representations of language that can be applied to a wide range of natural language processing tasks, without requiring large amounts of task-specific data.