How do transformers improve upon traditional RNNs?

Transformers enhance performance over traditional Recurrent Neural Networks (RNNs) primarily by effectively managing long-range dependencies within text. RNNs, particularly the vanilla versions, process input data sequentially, which often leads to challenges when trying to capture relationships between words or tokens that are far apart in a sequence. This sequential dependency can hinder the network's ability to remember information over long distances, commonly leading to issues like vanishing gradients.

In contrast, transformers utilize a mechanism called self-attention, which allows them to weigh the importance of every part of the input data relative to one another, no matter their distance in the sequence. This approach makes it feasible to establish clearer connections between words that might be far apart, enabling the model to learn and generate more coherent and contextually relevant outputs. Furthermore, transformers can process all tokens simultaneously rather than sequentially, significantly improving training speed and model performance, particularly on longer sequences.

While the other options touch on aspects of RNNs, they do not accurately describe how transformers fundamentally improve upon them. For example, the method of processing data sequentially (option one) is actually characteristic of RNNs rather than a benefit of transformers. Similarly, the claim about reducing dataset size or being less complex in structure