The parameters of a Large Language Model (LLM) include model architecture, size, training data, and hyperparameters. These parameters, like weights, biases, and embedding vectors, influence how the model comprehends language, learns patterns, and generates text. Adjusting these parameters optimizes the model's performance, with larger models typically offering better capabilities but requiring more computational resources for training and deployment.