How are LLMs trained and what are the stages involved?

LLMs are typically trained through a multi-stage process involving pre-training and fine-tuning. In the pre-training phase, the model learns language representations from a large corpus using unsupervised methods, usually through masked language modeling or next-token prediction. This phase captures grammar, semantics, and world knowledge. The fine-tuning phase involves supervised training on a narrower dataset tailored to specific tasks, utilizing labeled examples to improve the model’s performance on those tasks. Additional techniques like reinforcement learning from human feedback (RLHF) can further align the model’s outputs with human preferences.