Salesforce AI Specialist Practice Exam

What are the variables that LLMs learn during their training process called?

1. Parameters

2. Predictions

3. Outputs

4. Inputs

The correct answer is: Parameters

The variables that large language models (LLMs) learn during their training process are referred to as parameters. In the context of machine learning, parameters are the internal configuration values that the model adjusts based on the data it processes. During training, the model uses a large dataset to learn these parameters, which influence how it interprets input data and generates outputs. Parameters are crucial because they determine how the model behaves when making predictions or generating text. The process of learning these parameters involves optimizing them through techniques like backpropagation, which minimizes the difference between the model's predictions and the actual outcomes in the training data. Other terms such as predictions, outputs, and inputs refer to different aspects of the model's functioning. Predictions are the results generated by the model when it processes input data; outputs are the final results provided by the model after processing the inputs; and inputs are the data fed into the model for it to process. Thus, parameters are specifically the learned values that dictate how the model operates and generates its responses.