Decoder
Definition
A decoder transforms internal representations into an output within an AI architecture.
A decoder is part of some AI models that generates output steps from an internal representation. In the encoder-decoder approach of the Transformer paper, it processes, among other things, previous information for a target output.
Simply explained
An interpreter formulates a sentence in another language from a understood message. The comparison suggests output; a neural decoder does not operate with human language understanding.
Why it matters in practice
Distinguishing encoder and decoder helps classify model architectures when introducing language AI.
Example from business
Possible example: A development team documents how a translation model generates the target sentence step-by-step from an input.
Limits and pitfalls
- Decoder can mean something different in other fields. No guaranteed capabilities can be inferred from the architecture name alone.
Origin & history
The Transformer paper first submitted in 2017 describes an encoder-decoder architecture. The arXiv version v7 read here is from 2023.
Use in mid-sized companies
- Possible application areas include model architecture comparisons, technical training, and analysis of text generation processes.
Frequently asked questions
Is every decoder part of a translation model?
No, the term is broader.
Do all Transformers work with encoder and decoder?
No, model architectures can vary.
Sources
- Attention Is All You Need · Vaswani et al. (2023): Attention Is All You Need
- Wie funktionieren Transformer-Modelle? · Hugging Face (o. J.): Wie funktionieren Transformer-Modelle?
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