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.