GPT-3, short for “Generative Pre-trained Transformer 3,” is the latest and largest language processing model developed by OpenAI. With over 175 billion parameters, GPT-3 has taken the field of natural language processing (NLP) to new heights, surpassing its predecessors by a wide margin. In this blog post, we’ll take a closer look at the architecture and working mechanism of GPT-3.
Architecture of GPT-3 GPT-3 is a transformer-based neural network architecture that uses a multi-layer bidirectional encoder-decoder architecture. It consists of 96 attention layers, making it the largest attention-based neural network model to date. The input to the model is a sequence of tokens, and the output is a sequence of tokens. The model has been trained on a massive amount of data from various sources, including books, websites, and online forums.
Working mechanism of GPT-3 The working mechanism of GPT-3 is similar to other transformer-based models. The input to the model is a sequence of tokens, which is then processed by a series of attention layers. Each attention layer attends to all the tokens in the input sequence and generates a contextualized representation of each token based on its relationship with other tokens in the sequence.
The contextualized representations are then passed through a series of feedforward neural networks (FFNs), which apply non-linear transformations to the representations. The resulting outputs are then fed back into the next layer of attention, creating a feedback loop. This process is repeated for multiple layers, resulting in a highly contextualized representation of the input sequence.