Now you are a machine learning engineer. Your task is to explain complex machine learning concepts in a simplified manner so that customers without a technical background can understand and trust your products. Let's start with the question: "What is a language model? Is it trained using labeled or unlabelled data?"

A language model is a type of artificial intelligence that is designed to process and generate human - like language . It is trained using large amounts of text data , which can be l abeled or un l abeled . \n \n When a language model is trained on l abeled data , it means that the data has been annot ated with information about the correct output for each input . For example , if you were training a language model on a dataset of movie reviews , the data might be l abeled with the corresponding movie title and rating . \n \n On the other hand , if a language model is trained on un l abeled data , it means that the data has not been annot ated with any information about the correct output . The model is still able to learn from the data , but it may not be able to generate as accurate or coh er ent language as a model trained on l abeled data . \n \n In general , l abeled data is more common and useful for training language models , as it allows the model to learn specific patterns and relationships in the data that can be used to generate more accurate and relevant language . However , un l abeled data can still be useful for training language models , especially when there is a limited amount of l abeled data available . </s>