Transformer (deep learning)

In deep learning, the transformer is a family of artificial neural network architectures based on the multi-head attention mechanism, in which input data such as text, images, or audio, is converted to a sequence of numerical representations called tokens, and each token is converted into a vector through an embedding layer. At each layer, each token is then contextualized within the scope of the context window with other (unmasked) tokens via a parallel multi-head attention mechanism, allowing the signal for key tokens to be amplified and less important tokens to be diminished. Because self-attention alone is permutation-invariant, transformers inject positional information, typically through positional encodings or learned positional embeddings, so token order can affect the output. Because transformers do not process tokens one at a time, their computations can be parallelized across sequence positions during training more readily than those of recurrent neural networks like long short-term memory (LSTM). Later variations have been widely adopted for training large language models (LLMs) on large (language) datasets. Modern transformer designs are commonly grouped into encoder-only, decoder-only, and encoder-decoder variants, depending on whether they are optimized for representation learning, autoregressive generation, or conditional sequence-to-sequence tasks. The original version of the transformer architecture was proposed in the 2017 paper "Attention Is All You Need" by researchers at Google. The predecessors of transformers were developed as an improvement over previous architectures for machine translation, but have found many applications since. They are used in large-scale natural language processing, computer vision (vision transformers), reinforcement learning, audio, multimodal learning, robotics, and playing chess. It has also led to the development of pre-trained systems, such as generative pre-trained transformers (GPTs) and BERT (bidirectional encoder representations from transformers).