Understanding Transformers: Architecture, Attention Mechanism, Training and Inference

Transformers have become the dominant architecture for natural language processing (NLP) after their introduction in the paper "Attention is All You Need". They rely on multi‑head attention to model relationships between all tokens in a sequence, enabling parallel processing and long‑range dependency capture.

The model consists of stacked encoder and decoder layers. Each encoder layer contains a self‑attention sub‑layer, a feed‑forward network, residual connections, and layer‑norm. Decoder layers add an additional encoder‑decoder attention sub‑layer.

During training, the source (input) sequence and the target (output) sequence are both fed into the model. The target sequence is provided to the decoder (teacher forcing) so that the model learns to predict the next token while having access to the correct previous tokens, which speeds up training and avoids error accumulation.

In inference, only the source sequence is available. The decoder generates tokens step‑by‑step, feeding the previously generated tokens back into itself until an end‑of‑sentence token is produced. Because the encoder outputs remain constant, the encoder computation is performed only once.

Compared with recurrent neural networks (RNNs) and convolutional models, Transformers enable full parallelism, handle long‑distance dependencies without degradation, and achieve higher accuracy on tasks such as machine translation, text summarization, question answering, named‑entity recognition, and classification.

The article concludes that Transformers are the foundation for modern large language models (e.g., BERT, GPT) and that future posts will explore their internal mechanisms in greater depth.