GPT

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Generative Pre-trained Transformer 1 (GPT-1) is OpenAI's first large language model (LLM) in its GPT series of models, developed following Google's invention of the transformer architecture in 2017. In June 2018, OpenAI released a paper titled "Improving Language Understanding by Generative Pre-Training", in which they introduced that initial model along with the term generative pre-trained transformer (GPT). Up to that point, the best-performing neural NLP models primarily employed supervised learning from large amounts of manually labeled data. This reliance on supervised learning limited their use of datasets that were not well-annotated, in addition to making it prohibitively expensive and time-consuming to train extremely large models; many languages (such as Swahili or Haitian Creole) are difficult to translate and interpret using such models due to a lack of available text for corpus-building. In contrast, a GPT's "semi-supervised" approach involved two stages: an unsupervised generative "pre-training" stage in which a language modeling objective was used to set initial parameters, and a supervised discriminative "fine-tuning" stage in which these parameters were adapted to a target task. The use of a transformer architecture, as opposed to previous techniques involving attention-augmented RNNs, provided GPT models with a more structured memory than could be achieved through recurrent mechanisms; this resulted in "robust transfer performance across diverse tasks".