Decoder-Only Transformer Model

Context:
- It can (typically) be responsible for generating sequences based on input representations, without encoding them first.
- It can (typically) employ a Masked Self-Attention Mechanism to implicitly encode the context within its decoder layers.
- It can (often) be an Auto-Regressive Model.
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Example(s):
- an Decoder-Only Transformer-Based Language Model, such as: PaLM Model, GPT Model, GPT-J.
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Counter-Example(s):
- an Encoder-Only Transformer Model.
- an Encoder-Decocer Transformer Model.
See: Decoder Transformer, Self-Attention.

References

chat
- A Decoder-Only Transformer Model is a type of Transformer Model that solely consists of a decoder architecture. The primary function of this model is to generate sequences based on input representations without encoding them first. Decoder-Only Transformer Models have gained popularity in various Natural Language Processing (NLP) tasks, including text generation, machine translation, and summarization. A notable example of a Decoder-Only Transformer Model is the OpenAI's GPT series, such as GPT-2 and GPT-3.

chat
- While GPT-3 is indeed a Decoder-Only Transformer Model, it does not rely on a separate encoding system to process input sequences. Instead, the model employs a self-attention mechanism to implicitly encode the context within its decoder layers.
  In GPT-3, the input tokens are processed sequentially through the decoder layers, each consisting of a self-attention mechanism and position-wise feed-forward networks. The self-attention mechanism helps the model to learn and understand the relationships between tokens in the input sequence. As the tokens pass through the layers, the model generates context-aware representations by accumulating information from the surrounding tokens, considering their positions and relationships.
  Although GPT-3 does not have a dedicated encoder component like an Encoder-Decoder Transformer Model, its decoder architecture effectively encodes and processes the input sequences while generating coherent output.

https://ai.stackexchange.com/a/27040
- QUOTE: ... 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. ...