How is GPT-3 trained?
GPT-3 (Generative Pre-training Transformer 3) is trained using a process called unsupervised pre-training. This process involves training a large neural network on a massive corpus of text data without any explicit labels or supervision. The goal of this pre-training is to learn the patterns and structure of the text data so that the model can generate text that resembles human language.
The training process for GPT-3 involves several steps:
Data collection: The first step is to collect a large corpus of text data. This data is used to train the model and can come from a variety of sources, such as books, articles, websites, and more.
Pre-processing: The collected data is then pre-processed to clean and prepare it for training. This includes tasks such as tokenization, lowercasing, and removing special characters.
Model architecture: GPT-3 uses a transformer-based architecture, which is composed of an encoder and a decoder. The encoder takes in the input text and generates a set of hidden representations, while the decoder generates the output text.
Training: The model is then trained on the pre-processed text data using an unsupervised learning approach. The model learns to predict the next word in a sentence given the previous words.
Fine-tuning: GPT-3 is fine-tuned on a smaller dataset for specific tasks such as text generation, text completion, question answering, and more.
The training process for GPT-3 is computationally intensive, and requires a lot of computational resources, such as powerful GPUs and large amounts of memory. Training GPT-3 models usually takes weeks or even months and requires large amount of labeled data.
It’s important to note that GPT-3 is just the latest version of GPT, OpenAI has released other versions like GPT-2, GPT-1 and more. Each version is larger and more advanced than the previous one, and uses more data and computational resources for training.