The transformer model, introduced in the paper "Attention Is All You Need" by Vaswani et al. in 2017, represents a paradigm shift in how sequential data is processed by neural networks. Unlike its predecessors, which relied on recurrent or convolutional layers, the transformer utilizes a mechanism called self-attention to weigh the importance of different parts of the input data differently. This approach allows the model to process all parts of the input simultaneously (in parallel), significantly improving efficiency and performance on tasks such as language translation, text summarization, and content generation. The architecture is made up of an encoder and decoder, each consisting of multiple layers of self-attention and position-wise fully connected feed-forward networks. Transformers have become the foundation for many state-of-the-art natural language processing models, including BERT, GPT (Generative Pre-trained Transformer), and T5.
The concept of the transformer was first introduced in 2017 and quickly gained popularity for its efficiency and effectiveness in handling long-range dependencies in text, outperforming existing models on a variety of tasks.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin are credited with the development of the transformer model, making a significant impact on the field of machine learning and natural language processing.