Transformer Model
Date of Addition
22 March 2025
A neural network architecture introduced in the 2017 Google paper "Attention Is All You Need" that uses self-attention mechanisms to process sequential data. Transformers can determine relationships between elements in a sequence without the need for recurrent connections, enabling more efficient parallel processing.
Transformer models consist of encoder and decoder components working together with an attention mechanism that weighs the importance of different elements in the input sequence. This architecture has proven remarkably versatile, powering advances in natural language processing, computer vision, and multimodal AI. Transformers form the foundation of large language models (LLMs) like ChatGPT and have enabled significant breakthroughs in AI's ability to understand and generate human-like perceivable content. Their ability to process all elements of a sequence in parallel (rather than sequentially) has dramatically improved training efficiency compared to earlier architectures.
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