The architecture of a large language model (LLM) typically consists of a multi-layered neural network, with each layer learning progressively more complex features of language data. The basic building block of the network is a neuron or node, which receives input from other neurons and produces an output based on its learned parameters.

The most common architecture used in LLMs is the transformer architecture, which was introduced in the paper "Attention Is All You Need" by Vaswani et al. (2017). The transformer architecture consists of an encoder and a decoder, with each consisting of several layers of self-attention and feedforward neural networks.

The encoder processes the input text, learning representations of each word or token in the context of the entire input sequence. The self-attention mechanism allows the model to focus on different parts of the input sequence when processing each token, allowing it to capture complex dependencies between words.

The decoder takes the encoded input representation and generates output text, such as a translation or text completion. The decoder also uses a self-attention mechanism to generate output words, allowing it to focus on relevant parts of the encoded input representation.

More recent LLM architectures, such as GPT-3, use a similar transformer-based architecture, but with significantly more layers and parameters. GPT-3 has 175 billion parameters and consists of 96 layers, making it one of the largest and most powerful LLMs to date.