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In 1990, the Elman network, using a recurrent neural network, encoded each word in a training set as a vector, called a word embedding, and the whole vocabulary as a vector database, allowing it to perform such tasks as sequence-predictions that are beyond the power of a simple multilayer perceptron.Timeline of natural language processing models Timeline

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). This architecture is now used not only in natural language processing and computer vision, but also in audio and multi-modal processing. Though the transformer paper was published in 2017, the softmax-based attention mechanism was proposed in 2014 for machine translation, and the Fast Weight Controller, similar to a transformer, was proposed in 1992. 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.

Input text is split into n-grams encoded as tokens and each token is converted into a vector via looking up from a word embedding table. It is notable for not containing any recurrent units, and thus requires less training time than previous recurrent neural architectures, such as long short-term memory (LSTM), and its later variation has been prevalently adopted for training large language models on large (language) datasets, such as the Wikipedia corpus and Common Crawl.

A transformer is a deep learning architecture based on the multi-head attention mechanism.

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