What is the transformer architecture used in LLMs?

The Transformer architecture is a fundamental component in Large Language Models (LLMs). It was introduced in the paper "Attention is All You Need" by Vaswani et al. in 2017. Here's a simplified breakdown of the Transformer architecture:

• Encoder-Decoder Structure: The Transformer follows an encoder-decoder architecture. The encoder processes the input sequence, and the decoder generates the output sequence.

• Self-Attention Mechanism: Unlike recurrent neural networks (RNNs) or Long Short-Term Memory (LSTM) networks that process input sequentially, the Transformer uses a self-attention mechanism to weigh the importance of input words with respect to each other. This allows the model to capture dependencies between words regardless of their distance in the sequence.

• Position-wise Feed-Forward Network (FFN): After the self-attention mechanism, a position-wise FFN is applied to each position independently. This consists of two linear transformations with a ReLU activation in between.

• Add & Norm: Both the self-attention and feed-forward layers are surrounded by a residual connection followed by layer normalization. This is denoted as "Add & Norm" in the architecture.

• Multi-Head Attention: To allow the model to focus on different parts of the sequence simultaneously, the self-attention mechanism is extended to multi-head attention. Each head attends to different parts of the sequence, and the outputs are concatenated and projected.

In the context of LLMs, the Transformer architecture enables the model to understand and generate human-like text by capturing complex dependencies between words in a sequence.