So, you’ve seen some amazing GPT-3 demos on Twitter (machine-made Op-Eds, poems, articles, even working code). But what’s going on under the hood of this incredible model? Here’s a (brief!) look inside.
GPT-3 is a neural-network-powered language model. Alanguage model is a model that predicts the likelihood of a sentence existing in the world. For example, a language model can label the sentence “I take my dog for a walk” as more probable to exist (i.e. on the Internet) than the sentence “I take my banana for a walk.” This is true for sentences as well as phrases and, more generally, any sequence of characters.
Like most language models, GPT-3 is elegantly trained on an unlabeled text dataset (in this case, Common Crawl ). Words or phrases are randomly removed from the text, and the model must learn to fill them in using only the surrounding words as context. It’s a simple training task that results in a powerful and generalizable model.
The GPT-3 model architecture itself is atransformer-based neural network. This architecture became popular about 2–3 years ago, and is the basis for the popular NLP modelBERT. From an architecture perspective, GPT-3 is not actually very novel! So what makes it so special and magical?
IT’S REALLY BIG. I mean really big. With 175 billion parameters, it’s the largest language model ever created (GPT-2 had only 1.5 parameters!), and was trained on the largest dataset of any language model. This, it appears, is the main reason GPT-3 is so impressive.
And here’s the magical part. As a result, GPT-3 can do what no other model can do (well): perform *specific* tasks without any special tuning. You can ask GPT-3 to be a translator, a programmer, a poet, or a famous author, and it can do it with fewer than 10 training examples. Damn .
Most other models (like BERT) require an elaborate fine-tuning step, where you gather thousands of examples of (say) French-English sentence pairs to teach it how to do translation. With GPT-3, you don’t need to do that fine-tuning step. This is the heart of it. This is what gets people excited about GPT-3: custom language tasks without training data.
Today, GPT-3 is in private beta, but boy can I not wait to get my hands on it.
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