In AI and machine learning, parameters are intrinsic characteristics or variables that the model learns from the training data, and uses to structure its predictions.
For GPT-3, a text-generating AI model developed by OpenAI, the term "175 billion parameters" refers to the immense number of factors that the model considers when predicting the next word in a sentence.
More specifically, GPT-3 is a "transformer-based" model composed of multiple layers where each layer consists of self-attention and feed-forward neural network modules. These modules contain weights and biases (the parameters) that are adjusted during training.
These parameters capture patterns in the relationship between words which includes context, grammar rules, facts about the world, and even some amount of reasoning ability.
To put it in perspective, this large number of parameters is part of what makes GPT-3 so powerful, it has significantly more parameters than GPT-2, its predecessor, which had 1.5 billion parameters.
However, a great number of parameters also makes the model incredibly complex and more challenging to train. This is the reason why the existing language models with such scale exist predominantly in research labs and large tech companies, because they can afford the computational resources required.
