A research team is considering two different training strategies to build a language model using a large corpus of unlabeled text. Strategy A involves first training a preliminary model on a small, human-labeled 'seed' dataset, then using that model's predictions to create labels for the unlabeled text, and finally retraining the model on this newly labeled data. Strategy B involves no initial seed dataset; instead, it creates training tasks directly from the unlabeled text itself (e.g., by mask

Choosing a Training Methodology for a Foundational Model

A key difference between self-training and self-supervised pre-training is that self-training requires an initial model trained on a small set of labeled data to begin the learning process, whereas self-supervised pre-training can start with a randomly initialized model and only unlabeled data.