Masked Language Models

Masked Language Models (MLMs) are a type of neural network architecture used primarily in Natural Language Processing (NLP). They are designed to predict missing words in a sentence by masking certain words and training the model to infer them based on the context provided by the surrounding words. This approach allows MLMs to capture complex language patterns and dependencies, making them effective for tasks such as text generation, sentiment analysis, and language translation. A well-known example of an MLM is BERT (Bidirectional Encoder Representations from Transformers), which has significantly advanced the state of the art in various NLP benchmarks.