How does the Transformer architecture address the limitations of traditional RNNs in natural language processing tasks, and what are the key innovations that enable its superior performance?

The Transformer architecture, introduced by Vaswani et al., eliminates the sequential dependency inherent in RNNs, enabling parallel processing and thus significantly reducing training times. Key innovations include the self-attention mechanism, which allows the model to weigh the importance of different words in a sentence, and the use of positional encodings to maintain the order of the sequence. This architecture’s ability to capture long-range dependencies more effectively than RNNs leads to superior performance in NLP tasks.