In the influential paper "Attention is All You Need", the authors present an original architecture known as the Transformer. This architecture shifts the paradigm for addressing sequence-to-sequence challenges, which has traditionally relied on recurrent neural networks (RNNs). While useful, RNNs had limitations in dealing with long sequences, leading to the development of attention mechanisms as a means of overcoming these problems.

Despite the progress made with attention mechanisms, a major limitation remained in the field of RNNs. In particular, sequences had to be treated one element at a time, which for large data sets resulted in a computationally expensive and time-consuming process. This paper proposes the Transformer model for sequence processing, which avoids the use of RNNs and relies exclusively on the attention mechanism.

This groundbreaking design allows for parallel processing of input sequence components, which significantly improves computational efficiency and reduces processing time. Furthermore, the Transformer model is adept at recognising long-range dependencies within input sequences, further enhancing its effectiveness in sequential tasks. Essentially, the paper presents a novel architecture that changes the way such problems are approached, highlighting the importance and power of attention mechanisms in contemporary deep learning methods.
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