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After looking into transformers, BERT, and GPT-2, from what I understand, GPT-2 essentially uses only the decoder part of the original transformer architecture and uses masked self-attention that can only look at prior tokens.

Why does GPT-2 not require the encoder part of the original transformer architecture?

GPT-2 architecture with only decoder layers

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  • $\begingroup$ You say "GPT-2 essentially uses only the encoder part of the original transformer architecture", but maybe you meant "decoder"? $\endgroup$ – nbro Mar 27 at 20:42
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    $\begingroup$ @nbro Correct, it shoud be "decoder", sorry for the mistake, updated the question. $\endgroup$ – Athena Wisdom Mar 27 at 20:53
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GPT-2 is a close copy of the basic transformer architecture.

GPT-2 does not require the encoder part of the original transformer architecture as it is decoder-only, and there are no encoder attention blocks, so the decoder is equivalent to the encoder, except for the MASKING in the multi-head attention block, the decoder is only allowed to glean information from the prior words in the sentence. It works just like a traditional language model as it takes word vectors as input and produces estimates for the probability of the next word as outputs but it is auto-regressive as each token in the sentence has the context of the previous words. Thus GPT-2 works one token at a time.

BERT, by contrast, is not auto-regressive. It uses the entire surrounding context all-at-once. GPT-2 the context vector is zero-initialized for the first word embedding.

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