As the “G” in their names indicates, the GPT models are generative: they generate original text output in response to the text input they are fed. This is an important distinction between the GPT class of models and the BERT class of models. BERT, unlike GPT, does not generate new text but instead analyzes existing text .
With 1.5 billion parameters, GPT-2 was the largest model ever built at the time of its release. Published less than a year later, GPT-3 was two orders of magnitude larger: a whopping 175 billion parameters.in the human brain). As a point of comparison, the largest BERT model had 340 million parameters.
The reason such large training datasets are possible is that transformers use self-supervised learning, meaning that they learn from unlabeled data. This is a crucial difference between today’s cutting-edge language AI models and the previous generation of NLP models, which had to be trained with labeled data.
Without anyone quite planning for it, this has resulted in an entirely new paradigm for NLP technology development—one that will have profound implications for the nascent AI economy.In the first phase, a tech giant creates and open-sources a large language model: for instance, Google’s BERT or Facebook’s RoBERTa.
In the second phase, downstream users—young startups, academic researchers, anyone else who wants to build an NLP model—take these pre-trained models and refine them with a small amount of additional training data in order to optimize them for their own specific use case or market. This step is referred to as “fine-tuning.”
This makes these pre-trained models incredibly influential. So influential, in fact, that Stanford University has recently coined a new name for them, “foundation models”, and launched an entire academic program devoted to better understanding them: the
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