Transformers are a type of neural network architecture that has significantly advanced the field of natural language processing (NLP). They were introduced in the influential paper “Attention is All You Need” by Google Research and have since become the foundation for many state-of-the-art models in NLP. The architecture is characterized by its encoder-decoder structure and the use of attention mechanisms, which allow the model to weigh the importance of different words in a sentence when making predictions or generating text ²³.

One of the key advantages of Transformers is their ability to process data in parallel, which makes them more efficient than previous architectures like Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks. This parallel processing capability, combined with the attention mechanism, enables Transformers to capture long-range dependencies in text more effectively ³.

Overall, Transformers have revolutionized NLP and are widely used in various applications, including machine translation, text summarization, and sentiment analysis ³.