How Language Models Work

Language models are trained on vast datasets of text to statistically learn the properties of human languages. They predict the next word in a sequence using the previous words as context. As they process more data, the predictions improve.

Key components include:

• Vocabulary: The list of words known to the model.
• Context Window: The preceding words used to predict the next word.
• Neural Network Architecture: Models like RNNs, LSTMs and Transformers.
• Training Data: Large text corpora like books, news, web content etc.