Pre-training is the process of training an LLM on a massive dataset of unlabeled text data, such as books, articles, and websites. The goal of pre-training is to teach the model the statistical patterns, semantic relationships, and linguistic structures present in human language. This is done by using unsupervised learning techniques, where the model learns from the data without the need for explicit labels or guidance.
Training is the process of fine tuning a pre-trained LLM on a smaller dataset of labeled data, specific to the task that the model is intended to perform. This is done using supervised learning techniques, where the model learns to associate the input data with the corresponding output labels.
The key difference between pre-training and training is that pre-training focuses on general language understanding, while training focuses on task-specific performance. Pre-training gives the LLM a strong foundation in the language, which makes it easier to train the model on specific tasks later on.
See Also: Large Language Models, Full fine tuning and shallow fine tuning