Chapter 04.01: ARLMs vs. MLM
ARLM (Auto-Regressive Language Modeling) and MLM (Masked Language Modeling) are both self-supervised learning objectives used in pretraining transformer-based language models like BERT. ARLM involves predicting the next word in a sequence given the previous context, while MLM involves masking some of the input tokens and predicting them based on the surrounding context. Both methods leverage self-supervision, where the model learns from the data itself without requiring explicit labels, enabling it to capture meaningful representations of language.