7.3 Transformer model

Attention is all you need

The Transformer model was introduced in the Google research paper titled "Attention is all you need" published in December 2017, which provided the ‘Attention mechanism’ as one the key breakthroughs that enabled the development of LLMs [44]. This paper proposed a new Neural Network architecture called the Transformer, which replaced the traditional Recurrent Neural Network (RNN) architecture that had been widely used for natural language processing tasks.

The Transformer architecture is based on the idea of attention, which allows the model to selectively focus on different parts of the input sequence during processing. This attention mechanism helps the model to better capture long-range dependencies between words in a sentence, which is crucial for natural language processing tasks such as machine translation, language modeling, and text generation. 

The Transformer architecture consists of a series of layers, each of which contains a multi-head attention mechanism and a feed-forward Neural Network. The multi-head attention mechanism allows the model to attend to different parts of the input sequence simultaneously, while the feed-forward network applies non-linear transformations to the attended input to generate the output.

The Transformer architecture has several advantages over the traditional RNN-based models. First, it allows for parallel processing of input sequences, which makes training much faster and more efficient. Second, it avoids the problem of vanishing gradients that can occur in RNNs when processing long sequences. Finally, it achieves state-of-the-art results on a wide range of natural language processing tasks, including machine translation, language modeling, and text generation.