Transformers: Age of Attention

In 2017, researchers introduced the Transformer model in the paper "Attention is All You Need," revolutionizing natural language processing (NLP). Prior models like RNNs and LSTMs processed words sequentially, limiting their ability to handle long sentences, slowing training, and hindering parallel processing. The Transformer solved these issues by using self-attention, allowing the model to focus on important words regardless of their position in a sentence. This made the Transformer faster and more scalable, especially by leveraging parallelization. It eliminated the need for sequential word processing and improved the understanding of complex relationships between words. The model's encoder-decoder architecture efficiently processes input sequences into outputs like translations. Key features like multi-head attention allow the model to capture different aspects of meaning in a sentence simultaneously. The decoder generates translations step-by-step by focusing only on preceding words, ensuring accuracy. This architecture has become the foundation for many state-of-the-art models like BERT and GPT, vastly improving performance in various NLP tasks.