Understanding Transformer Architecture of LLM: Attention Is All You Need

Transformer architecture became a turning point for language modeling because it replaces sequential processing with self-attention, enabling parallel computation while still capturing word-to-word relationships across an entire sentence. That shift matters because older sequence models such as recurrent neural networks, LSTMs, and GRUs process tokens step by step, which slows training and makes long-range dependencies harder to learn efficiently at scale. By contrast, Transformers can be trained faster on large datasets and handle complex language patterns without the same bottleneck of strict temporal recurrence.

The core idea behind the 2017 paper “Attention Is All You Need” (Ashish Vaswani and collaborators at Google Brain, Google Research, and the University of Toronto) is a self-attention mechanism that lets the model weigh how much each token should attend to every other token. The architecture is organized into two main stacks: an encoder and a decoder. In a machine translation example, the encoder reads the source sentence (e.g., English) and the decoder generates the target sentence (e.g., Spanish) one token at a time. Both stacks rely on embeddings to convert tokens into numeric vectors, but they also need positional information so the model can distinguish “Sunset over the ocean” from “Ocean over the Sun.” Positional encoding injects word-order signals into the input embeddings, allowing attention to incorporate both meaning and sequence.

Inside the encoder, each identical layer contains two sublayers: multi-head self-attention and a position-wise feed-forward network. Multi-head attention runs several attention “heads” in parallel, where each head can focus on different relationships—for instance, one head might connect “Sunset” with “ocean,” while another emphasizes how “over” links the two. Mathematically, attention uses query, key, and value vectors (Q, K, V) to compute attention scores, applies a softmax to turn those scores into weights, and then combines values accordingly. The outputs from all heads are merged, then added back to the original representation (a residual connection) and normalized to stabilize training.

The position-wise feed-forward network further transforms each token representation independently at every position using two linear layers with a ReLU activation in between. This design lets the model refine what attention learned, while keeping computation efficient because the same feed-forward structure is applied across positions.

The decoder mirrors the encoder but adds a third sublayer: multi-head attention over the encoder’s output. During generation, the decoder uses self-attention to consider previously generated tokens and encoder-decoder attention to align the emerging translation with relevant parts of the source sentence. The final stages map decoder outputs to vocabulary-sized scores via a linear layer, convert them into probabilities with softmax, and select the most likely next token—continuing until an end-of-sequence token appears.

Transformers have since become practical tools for machine translation, text summarization, and tasks like identifying names and places in text, largely because their attention-based design improves parallelization and scalability compared with recurrent approaches.