What is the transformer architecture in LLMs?

The transformer architecture is a neural network design introduced in 2017 that underpins modern large language models (LLMs) like GPT and BERT. Its core innovation is the self-attention mechanism, which allows the model to analyze relationships between words in a sequence dynamically. Unlike older architectures (e.g., RNNs), transformers process all words in parallel, making them faster and better at capturing long-range dependencies. The architecture consists of stacked layers containing self-attention and feed-forward neural networks, with techniques like residual connections and layer normalization to stabilize training. Positional encodings are added to input embeddings to retain word order information, as transformers lack inherent sequential processing.

A key component is multi-head attention, which splits the input into multiple “heads” to focus on different types of relationships simultaneously. For example, in the sentence “The bank charges fees for river access,” one head might link “bank” to “fees” (financial context), while another connects “river” to “bank” (geographical meaning). Each attention head computes queries, keys, and values—matrices derived from input embeddings—to determine how strongly words influence each other. After attention, a feed-forward network applies non-linear transformations to refine features. These layers are repeated multiple times (e.g., 12 layers in BERT-base), allowing the model to build complex representations.

Transformers are used in two main configurations: encoder-only (e.g., BERT) for tasks like classification, and decoder-only (e.g., GPT) for text generation. Encoders focus on understanding input context, while decoders generate output token by token, using masked attention to prevent future word visibility during training. Practical applications include translation (input text is encoded, output is decoded stepwise) or summarization (encoder processes the article, decoder produces the summary). Developers often fine-tune pre-trained transformers by adding task-specific layers, leveraging their ability to handle varied data structures (text, code, etc.) through tokenization and embedding adaptations.