Transformer Architecture

The Transformer Architecture is a neural network model published by Google researchers in 2017, based exclusively on the self-attention mechanism, which revolutionized the field of natural language processing (NLP). The model's key characteristic is that it abandons previous recurrent and convolutional layers, allowing parallel processing of data (e.g., words in sentences) while also being able to map complex relationships between distant elements in input sequences. This technological breakthrough significantly improves the performance of translation programs, text generators, and other language models, as the system handles contextual dependencies and long-term memory tasks more efficiently. The Transformer Architecture forms the foundation of modern large language models (such as ChatGPT or Gemini), making it the most defining building block of contemporary generative artificial intelligence.