Member-only story
Understanding Transformer Architectures: Decoder-Only, Encoder-Only, and Encoder-Decoder Models
The Standard Transformer was introduced in the seminal paper “Attention is All You Need” by Vaswani et al. in 2017. The Transformer architecture has revolutionized natural language processing (NLP), becoming the backbone of most modern language models. Transformers are modular and versatile, enabling various configurations tailored to specific tasks. Three primary variant configurations are decoder-only, encoder-only, and encoder-decoder transformers. Each has unique characteristics, applications, and notable models.
What is the Standard Transformer?
The Standard Transformer consists of two main components:
- Encoder: Encodes the input sequence into a set of continuous representations.
- Decoder: Decodes the encoded representations to generate the output sequence.
Both components are built using multi-head self-attention mechanisms and feedforward neural networks, connected via residual connections and layer normalization.
1. Decoder-Only Transformers
What Are They?
Decoder-only transformers focus on generating sequences. The model is trained to predict the next token in a sequence given the previous tokens, making it unidirectional (left-to-right) in nature.
