Since its introduction, researchers have continued to explore and extend the Transformer architecture, leading to further advancements and innovations in the field of deep learning and language process.
Self-Attention Mechanism: The core component of the Transformer architecture is the self-attention mechanism. At a high level, self-attention allows the model to weigh the importance of different words in a sequence when processing each word. Given an input sequence, self-attention computes three vectors for each word: Query, Key, and Value. These vectors are linear projections of the input embeddings.
The attention scores between words are calculated by taking the dot product between the Query and Key vectors, followed by scaling and applying a softmax function to obtain attention weights. Ultimately, the weighted sum of the Value vectors, based on the attention weights, produces the output of the self-attention layer.
Multi-Head Attention: To enhance representational capacity and capture different aspects of the input, the Transformer architecture employs multi-head attention. This involves performing self-attention multiple times in parallel, each with its own set of learned Query, Key, and Value transformations. The outputs of the multiple attention heads are concatenated and linearly projected to produce the final output of the multi-head attention layer.
Positional Encoding: Unlike recurrent neural networks (RNNs) or convolutional neural networks (CNNs), the Transformer architecture does not inherently capture the order of words in a sequence. To address this limitation, positional encoding is introduced. Positional encoding vectors are added to the input embeddings, providing information about the position of each word in the sequence. Positional encoding can take various forms, such as sinusoidal functions or learned embeddings, to ensure that the model can generalize to sequences of different lengths.
Feedforward Neural Networks (FFNs): In addition to self-attention layers, the Transformer architecture includes feedforward neural networks (FFNs) as a component of its encoder and decoder blocks. The FFN consists of two linear transformations separated by a non-linear activation function, such as the GELU (Gaussian Error Linear Unit) or ReLU (Rectified Linear Unit).FFNs provide the model with the capacity to capture complex patterns and interactions within the input sequences.
Encoder-Decoder Architecture: The Transformer architecture is typically used in an encoder-decoder setup for sequence-to-sequence tasks, such as machine translation or text summarization. The encoder processes the input sequence and generates a fixed-dimensional representation, while the decoder takes this representation and generates an output sequence token by token. Both the encoder and decoder consist of multiple layers of self-attention and FFN modules. During training, masking is applied to prevent the model from attending to future tokens in the input sequence, ensuring that the predictions are autoregressive and based only on previous tokens. In the encoder, masking is applied to prevent attention to future tokens in the input sequence, while in the decoder, additional masking is applied to prevent attention to subsequent positions in the output sequence.
Since its introduction, researchers have continued to explore and extend the Transformer architecture, leading to further advancements and innovations in the field of deep learning and language processing
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