Parameters are one of the defining characteristics of artificial intelligence models. The GPT-3 large language model of OpenAI has 175 billion parameters and the succeeding GPT-4 has more than trillions while the LLaMA foundation model of Meta Platforms has parameters ranging from 7 billion to 65 billion. These numbers also represent the model size and can be a general gauge of their capabilities and performance.
A Simplified Explainer of What Parameters Are in Artificial Intelligence Models
The given parameters of a particular model represent the weight of the various probabilities that it can produce. They are technically defined as variables whose values are adjusted during training to establish how input data gets transformed into the desired output. A single parameter is thereby a value that is earned and adjusted by an artificial intelligence algorithm during the training process to make decisions and predictions.
Remember that the value of the parameters has a critical role in determining the performance of a model and can have a significant impact on its accuracy, speed, and generalization capabilities. However, in some models, this is not the case. Meta Platforms explained that LLaMA can go head-to-head with GPT-3 despite having fewer parameters because its development focused on increasing the amount of training data instead of its model size.
There are two types of parameters. These are model parameters and hyperparameters. Model parameters are learned from the training data. A particular AI model looks into a dataset to set and adjust the parameters to make accurate predictions. Hyperparameters, on the other hand, are like instructions set by the user before the model starts learning. These hyperparameters are fixed and they control how the learning process happens.
It is important to reiterate the fact that an AI model is given parameters either before or during the training. Most models, especially foundation models such as large language models, have both model parameters and hyperparameters. A completed model has the best set of parameters that allows it to make more accurate predictions or better generate the desired outputs. Parameters fundamentally equip a model with prediction capabilities.
Take note that a parameter is generally considered a characteristic that can help in defining or categorizing a particular system. Understanding what parameters are in the realms of AI can still be difficult despite the aforementioned definitions. This is especially true for individuals with no background in artificial intelligence or computer science. An analogy can help in understanding them better and knowing their role in AI modeling.
Consider a particular AI model as a small device that can do a lot of things including recognizing and understanding image and speech. This device has these capabilities because it has been given a set of instructions that tells it how to categorize and identify visual data and how to interpret spoken communication. These instructions are like buttons or knobs placed during the training and are manipulated during an image or speech recognition process.
The provided instructions are the parameters. A 7-billion-parameter model means that it has 7 billion buttons or knobs that can be manipulated or set in various ways and orientations to make it work. These are like specific settings that help the particular model learn and understand things better. The bigger the value means more settings and better capabilities for handling complex tasks and making more accurate predictions.
Giving a particular AI model parameters involves providing it with explicit instructions before the training or training it through exposure to different images and spoken words for it to produce its own set of instructions. Remember that a hyperparameter is included prior to the training while a model parameter are produced during the training process. The completed model now knows how to transform input data into the desired output.