How do Transformers contrast to RNNs in the context of NLP?
Transformers and recurrent neural networks (RNNs) are two popular types of neural network architectures used in natural language processing (NLP).

One key difference between Transformers and RNNs is their approach to sequence modeling. RNNs process a sequence of inputs one at a time, where the current input is influenced by the previous hidden state of the network. In contrast, Transformers process the entire sequence at once, using self-attention mechanisms to learn relationships between all elements in the sequence.

Another difference is that RNNs are often used for tasks that require modeling sequential dependencies, such as language modeling, text generation, and machine translation. However, RNNs can be computationally expensive and are often limited by their ability to capture long-range dependencies.

On the other hand, Transformers have become popular in NLP because they can efficiently handle long sequences and have achieved state-of-the-art results on many NLP tasks, such as language modeling, machine translation, and question-answering. Transformers can also be used for tasks that do not necessarily require modeling sequential dependencies, such as text classification and sentiment analysis.

In summary, while RNNs are well-suited for modeling sequential dependencies, Transformers are more efficient in handling long sequences and have achieved state-of-the-art results in many NLP tasks.
What are some possible future advancements or modifications to Transformer architectures that could further improve their performance or efficiency in NLP tasks?