Masked Language Modeling (MLM) is a technique used in natural language processing (NLP) to train language models. It involves randomly masking a portion of the input text and then training the model to predict the original tokens based on the remaining context. This process helps the model to learn better representations of language and to improve its ability to generate realistic and coherent text.
MLM is a key component of many modern NLP models, including BERT, GPT-2, and RoBERTa. It has been shown to be effective for a variety of tasks, including machine translation, text summarization, and question answering.
How MLM Works
Here’s a simplified explanation of how MLM works:
Input text: A sentence or phrase is provided as input to the language model.
Masking: A portion of the input text is randomly masked by replacing certain tokens with a special token, typically represented by [MASK].
Prediction: The masked language model is tasked with predicting the original tokens based on the context of the remaining unmasked tokens.
Training: The model is trained to minimize the loss function, which measures the difference between the predicted tokens and the actual original tokens.
Over the course of training, the model learns to associate the masked tokens with their surrounding context, enabling it to better understand the relationships between words and phrases.
MLM is typically done during pre-training. However, it can also be used for fine tuning or domain adaptation.
See Also: PETM, Pre-training and training of LLMs