GPT is built on the transformer architecture, which was introduced by Vaswani et al. in the paper "Attention is All You Need." Transformers use a self-attention mechanism to capture relationships between words in a sequence.
Pre-training:
The "pre-trained" aspect of GPT refers to the model being trained on a massive amount of diverse data before being fine-tuned for specific tasks. During pre-training, the model learns to predict the next word in a sentence based on context.
Generative Model:
GPT is a generative model, meaning it can generate coherent and contextually relevant text. Given a prompt, it can generate a continuation that fits well with the input.
Scale:
One of the distinguishing features of GPT-3 is its enormous scale. It has 175 billion parameters, making it one of the largest language models ever created. The vast number of parameters allows it to capture complex patterns and relationships in data.
Zero-Shot and Few-Shot Learning:
GPT-3 exhibits zero-shot and few-shot learning capabilities. In zero-shot learning, the model can perform tasks it has not been explicitly trained on, given a prompt. In few-shot learning, it can generalize from a few examples to perform a task.
Applications:
GPT-3 has demonstrated strong performance across a wide range of natural language processing (NLP) tasks, such as language translation, text completion, question-answering, summarization, and more.
API Access:
OpenAI has provided API access to GPT-3, allowing developers to integrate the model into their applications and leverage its capabilities for various tasks.
