Masked Language Modeling

Masked language modeling (MLM) is a self-supervised learning technique that trains language models to predict masked words in a sentence, thereby learning rich contextual representations. Current research focuses on improving MLM's efficiency and effectiveness through adaptive masking strategies, curriculum learning, and novel architectures like transformers, often incorporating techniques like contrastive learning and attention mechanisms. These advancements are impacting various fields, enabling improved performance in tasks such as text generation, speech restoration, and data synthesis while also addressing challenges like bias mitigation and efficient pre-training.