Decoder-Only Neural Model Architecture: Difference between revisions

Decoder-Only Neural Model Architecture (view source)

1,266 bytes added , 18 March 2024

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-#REDIRECT [[Decoder-only Neural Model Architecture]]
+A [[Decoder-Only Neural Model Architecture]] is a [[Neural Model Architecture]] that utilizes only the decoder component of a traditional [[Encoder-Decoder Architecture]] for generating sequences of data.
+* <B>Context:</B>
+** It can often leverage a [[Transformer]]-based architecture, utilizing mechanisms such as [[self-attention]] to process input sequences directly for output generation.
+** It can be trained on large datasets to capture intricate patterns and relationships within the data, making it effective for tasks requiring nuanced understanding of context.
+** ...
+* <B>Example(s):</B>
+** [[GPT Architecture]], which utilizes a decoder-only transformer model for generating human-like text.
+** [[BERT Architecture]] as used in a generative capacity, despite its initial design as an encoder for understanding text representations.
+** ...
+* <B>Counter-Example(s):</B>
+** an [[Encoder-Only Model Architecture]].
+** A [[Seq2Seq Model Architecture]], which relies on both an encoder and a decoder for tasks such as machine translation.
+** A [[CNN Model Architecture]], which is primarily used for tasks involving image data and does not inherently generate sequences.
+* <B>See:</B> [[Sequence Generation]], [[Transformer Architecture]], [[Natural Language Generation]], [[Self-Attention Mechanism]].
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