Manual and automatic parameters in AI training and output
Language Models, such as GPT-3, have billions or trillions of parameters that allow them to understand and generate human-like text. These parameters are the numerical weights that the model uses to learn and represent language patterns during training. While some parameters, like the temperature in a generative model, are set explicitly for controlling behavior, the vast majority of parameters are learned automatically through a process called training, and humans do not set them manually. You can read more about the other manually-configured parameters here: https://michaelehab.medium.com/the-secrets-of-large-language-models-parameters-how-they-affect-the-quality-diversity-and-32eb8643e631 Manual Parameters: Adapted from the blog above, we see some manual parameters are: Some of the common LLM parameters are temperature, number of tokens, top-p, presence penalty, and frequency penalty. Temperature : Temperature is a hyperparameter used in generative language models. It controls th