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 requiring 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, by virtue of the parallelized processing of input sequence. A transformer is a deep learning architecture, initially proposed in 2017, that relies on the parallel multi-head attention mechanism.
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