What is the difference between encoder and decoder?

Encoders process entire inputs bidirectionally and produce representations (good for understanding tasks). Decoders generate output sequentially, only seeing previous tokens (good for generation tasks).

Why are GPT models decoder-only?

GPT models focus on text generation, which requires predicting the next token based on previous context. The decoder's causal attention pattern is perfect for this autoregressive generation approach.

What is autoregressive generation?

Autoregressive generation produces output one token at a time, where each new token depends on all previously generated tokens. This is how all modern text generation models work.

Can decoder models understand text too?

Yes, large decoder models like GPT-4 and Claude are excellent at understanding despite being decoder-only. The scale and training enable emergent understanding capabilities beyond pure generation.

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Decoder

The component of a transformer model that generates output sequences. GPT-style models are "decoder-only" architectures optimised for text generation.

In-Depth Explanation

In transformer architecture, the decoder is responsible for generating output sequences one token at a time. Modern generative AI models like GPT, Claude, and Llama are "decoder-only" architectures, meaning they consist entirely of decoder layers.

The decoder works through autoregressive generation:

Takes all previous tokens as input
Processes through multiple transformer layers with self-attention
Predicts the probability distribution for the next token
Samples from that distribution to select the next token
Appends the new token and repeats

Key characteristics of decoder models:

Causal attention: Can only attend to previous tokens (no "peeking ahead")
Autoregressive: Generate one token at a time, left to right
Versatile: Can be prompted for many different tasks
Efficient inference: Optimised for text generation workflows

Decoder-only models contrast with encoder-only models (like BERT, for understanding) and encoder-decoder models (like T5, for translation and summarisation).

Business Context

Decoder models excel at content generation, coding, and conversational AI - the most common business applications of generative AI.

How Clever Ops Uses This

We primarily work with decoder models for our Australian business clients, as they power the chatbots, content generators, and coding assistants most businesses need.

Example Use Case

"GPT models use decoder architecture to generate text one token at a time, each token informed by all previous tokens in the sequence."

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