What is a transformer and how does it work?

A transformer is a revolutionary neural network architecture proposed in 2017 by Vaswani et al.in their landmark paper"Attention Is All You Need",which has fundamentally transformed the field of deep learning,especially in natural language processing(NLP),computer vision,and multi-modal processing.Unlike traditional recurrent neural networks(RNNs)and their variants(e.g.,LSTMs,GRUs)that process sequences sequentially,the transformer relies primarily on the self-attention mechanism to model contextual relationships between all elements in an input sequence simultaneously,enabling parallel processing and more efficient capture of long-range dependencies.

The working principle of a transformer can be broken down into four core stages,with its overall structure consisting of an encoder stack and a decoder stack,each composed of multiple identical layers:

First,Input Embedding and Positional Encoding.The input sequence(e.g.,text tokens)is converted into dense vector representations(word embeddings),which capture semantic information of each token.Since the transformer lacks inherent sequential awareness,positional encoding is added to the embeddings to convey the order of tokens.This encoding uses sine and cosine functions of different frequencies to generate unique position vectors,preserving both absolute and relative positional relationships between tokens.

Second,Multi-Head Attention Mechanism—the core of the transformer.In each encoder and decoder layer,the input vectors are transformed into three separate matrices:Query(Q),Key(K),and Value(V).The attention score between each pair of tokens is calculated by taking the dot product of Q and the transpose of K,scaled by the square root of the dimension of K to avoid large values that might saturate the softmax function.The softmax function normalizes these scores to obtain attention weights,which determine the importance of each token to the current token."Multi-head"means this process is repeated across multiple parallel attention heads,allowing the model to focus on different types of contextual relationships simultaneously(e.g.,syntactic and semantic dependencies).

Third,Feed-Forward Neural Network(FFNN).After the multi-head attention layer,the output is passed through a fully connected feed-forward network,which applies a linear transformation followed by a non-linear activation function(typically ReLU)to each token independently.This layer further processes the contextualized representations to capture complex non-linear relationships within the sequence.

Fourth,Layer Normalization and Residual Connections.Both the multi-head attention layer and the FFNN layer are accompanied by residual connections(which add the input of the layer to its output)and layer normalization(which stabilizes training by normalizing the activations).In the decoder,an additional masked multi-head attention layer is included to prevent the model from"peeking"at future tokens during sequence generation(e.g.,in machine translation).

The transformer’s parallel processing capability addresses the inefficiency of RNNs,reducing training time significantly,while its self-attention mechanism enables it to model long-range dependencies more effectively.This architecture has become the foundation of modern large language models(LLMs)such as GPTs and BERT,as well as advanced models in computer vision and audio processing.