Decoder-Only Transformer-based Neural Language Model

A Decoder-Only Transformer-based Neural Language Model is a Transformer-based neural LM that exclusively employs the decoder component of the transformer architecture in language modeling.

• Context:
  • It can (typically) generate coherent and contextually relevant text in applications such as chatbots, automated content creation, and language translation.
  • It can have Emergent LM Properties, such as: sentiment analysis, summarization, and question answering.
  • It can range from being a Small Decoder-Only Transformer-based Neural Language Model to being a Large Decoder-Only Transformer-based Neural Language Model.
  • It can be effective in Long Text Sequence Generation Tasks (tasks that require the generation of long sequences of text) due to its ability to maintain context over longer passages.
  • It can be fine-tuned on domain-specific data to improve its performance in specialized areas such as legal, medical, or technical language generation.
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• Example(s):
  • GPT-1, one of the first.
  • GPT-3, a large-scale decoder-only transformer-based model known for its text generation capabilities.
  • GPT-4, an advanced iteration of GPT-3 with more layers and parameters, used for sophisticated language tasks.
  • Turing-NLG, another example of a decoder-only transformer-based language model, known for its large number of parameters and deep learning capabilities.
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• Counter-Example(s):
  • BERT LM, which is an encoder-only transformer-based neural language model, primarily used for understanding language rather than generating it.
  • ElMO LM, which is not a transformer-based model but rather relies on recurrent neural network architecture for language modeling.
• See: Transformer Architecture, Language Generation, Pre-trained Language Models, Fine-Tuning in Machine Learning, Natural Language Processing Systems.

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References

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[[Category:Natural Language Processi