Architecture
Autoregressive Model
A model that generates a sequence one element at a time, feeding each output back in as input for the next step.
In depth
A model that generates a sequence one element at a time, feeding each output back in as input for the next step. GPT-3 is described in its paper as an autoregressive language model with 175 billion parameters, and the Transformer decoder works the same way. Because tokens are produced in order, generation time grows with output length. Diffusion language models are designed to remove that bottleneck.
Primary source: arxiv.org
Example
In practice, developers reach for Autoregressive Model when they need the capability described above as part of an AI feature or workflow.
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FAQ
Common questions
What is Autoregressive Model?
A model that generates a sequence one element at a time, feeding each output back in as input for the next step.
Why does Autoregressive Model matter for AI developers?
Autoregressive Model sits in the Architecture part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Autoregressive Model?
Developers Digest publishes tutorials and videos that cover Architecture topics including Autoregressive Model. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
Architecture
Attention Sinks
Attention sinks are the first few tokens of a sequence, which LLMs learn to send large attention scores to even when those tokens carry no special meaning.
Architecture
Diffusion Models
A class of generative models that learn to create data by reversing a gradual noising process.
Architecture
SwiGLU
SwiGLU is a gated activation used in the transformer feed-forward block: the input goes through two linear projections, one passed through the Swish function, and the two results are multiplied element-wise before the output projection.
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