Masked language models are a type of neural network architecture used to predict masked (missing) words in a text sequence. By masking a portion of the input sentence, these models are trained to generate the most probable word that fits the context. This approach is commonly used for tasks like text completion, and sentiment analysis. It is a central component in transformers like BERT, which leverages this technique for efficient language understanding.