The right pic shows a stereo Leak amplifier. Models, used before the transformer models prevailed, processed the input tokens sequentially, maintaining a state vector representing all the tokens up to the current token in the input.The output transformer is the heart of a valve amplifier. The attention mechanism may be obtained by interposing a softmax operator and three linear operators (one for each of query, key, and value).
In a fast weight controller, a feedforward neural network ("slow") learns by gradient descent to control the weights of another neural network ("fast") through outer products of self-generated activation patterns called "FROM" and "TO" which corresponds to "key" and "value" in the attention mechanism. In 1992, Jürgen Schmidhuber published the fast weight controller as an alternative to RNNs that can learn "internal spotlights of attention," and experimented with using it to learn variable binding. Provided with enough training data, their attention mechanisms alone can match the performance of RNNs with attention added. Unlike RNNs, transformers do not have a recurrent structure. 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).īefore transformers, most state-of-the-art NLP systems relied on gated RNNs, such as LSTMs and gated recurrent units (GRUs), with various attention mechanisms added to them. This architecture is now used not only in natural language processing, computer vision, but also in audio, and multi-modal processing. Though the Transformer model came out in 2017, the core attention mechanism was proposed earlier in 2014 by Bahdanau, Cho, and Bengio for machine translation.
More specifically, the model takes in tokenized ( byte pair encoding) input tokens, and at each layer, contextualizes each token with other (unmasked) input tokens in parallel via attention mechanism. It is notable for requiring less training time compared to previous recurrent neural architectures, such as long short-term memory (LSTM), and 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 that relies on the attention mechanism.
0 kommentar(er)
