What do you understand by MLM in Natural Language Processing

In Natural Language Processing (NLP), MLM stands for "Masked Language Modeling." MLM refers to a type of language modeling task where certain tokens in a sentence are masked, and the model is trained to predict the masked tokens based on the surrounding context.

Here's how MLM works:

1. Masking Tokens: In MLM, a certain percentage of tokens in the input text are randomly selected and replaced with a special mask token, such as [MASK]. These masked tokens are then used as input to the model during training.

2. Context Window: The model is trained to predict the original tokens that were masked based on the surrounding context provided by the unmasked tokens in the input sequence. This requires the model to learn meaningful representations of words and their relationships within the context of the sentence.

3. Objective Function: The objective of the MLM task is to maximize the likelihood of predicting the correct tokens given the masked input tokens and the context provided by the unmasked tokens. This is typically done using maximum likelihood estimation (MLE) or cross-entropy loss as the training objective.

4. Training: During training, the model adjusts its parameters (e.g., weights in a neural network) to minimize the loss function and improve its ability to predict the masked tokens accurately. The training process involves iterative optimization using techniques such as gradient descent.

5. Fine-Tuning: After pre-training on a large corpus using MLM, the model can be fine-tuned on specific downstream tasks, such as text classification, named entity recognition, or sentiment analysis. Fine-tuning adapts the pre-trained MLM model to the target task by further adjusting its parameters on a smaller task-specific dataset.

MLM is a popular approach in modern NLP, especially with the rise of transformer-based architectures like BERT (Bidirectional Encoder Representations from Transformers) and RoBERTa (Robustly optimized BERT approach), which have achieved state-of-the-art performance on various NLP benchmarks. By pre-training models using MLM on large text corpora, researchers can effectively capture rich contextual information and semantic relationships between words, enabling the models to perform well on a wide range of NLP tasks.