Decoder-Only Transformer Model

A Decoder-Only Transformer Model is a decoder-only model (follows a decoder-only architecture) that is a transformer-based model.

• Context:
  • It can (typically) process input sequences through self-attention layers to capture contextual relationships.
  • It can (typically) generate output tokens in an auto-regressive manner using masked self-attention.
  • It can (typically) maintain sequential information through positional encodings and causal masking.
  • It can (typically) learn language patterns through unsupervised learning on large text corpuses.
  • It can (typically) perform next-token prediction based on previous tokens in the sequence.
  • It can (often) handle multi-task learning through task-specific prompts.
  • It can (often) leverage transfer learning for downstream tasks.
  • It can (often) generate coherent text through conditional generation.
  • It can (often) perform zero-shot learning and few-shot learning.
  • ...
  • It can range from being a Small Language Model to being a Large Language Model, depending on its parameter count.
  • It can range from being a Task-Specific Model to being a General-Purpose Model, depending on its training objectives.
  • ...
• Example(s):
  • Language Models, such as:
    • GPT Familys, such as:
      • GPT-4 for general-purpose text generation.
      • GPT-3 for natural language processing.
      • GPT-2 for text completion.
    • PaLM Familys, such as:
      • PaLM 2 for multilingual capabilitys.
      • PaLM for reasoning tasks.
  • Domain-Specific Models, such as:
    • Code Generation Models, such as:
      • Codex for programming assistance.
    • Text-to-Text Models, such as:
      • GPT-J for open-source deployments.
  • ...
• Counter-Example(s):
  • Encoder-Only Transformer Model, which focuses on input understanding without generation capability.
  • Encoder-Decoder Transformer Model, which uses separate encoder and decoder components.
  • Recurrent Neural Network, which processes sequences using recurrent connections rather than self-attention.
• See: Transformer Architecture, Self-Attention Mechanism, Language Model, Neural Architecture, Sequence Generation.

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References

2023

• 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.

2023

• 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.

2021

• 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. ...

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2018

• (Radford et al., 2018) ⇒ Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. (2018). “Improving Language Understanding by Generative Pre-Training.”

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