Pre-training vs Fine-Tuning vs In-Context Learning of Large
Large language models are first trained on massive text datasets in a process known as pre-training: gaining a solid grasp of grammar, facts, and reasoning. Next comes fine-tuning to specialize in particular tasks or domains. And let's not forget the one that makes prompt engineering possible: in-context learning, allowing models to adapt their responses on-the-fly based on the specific queries or prompts they are given.
Pretraining vs Fine-tuning vs In-context Learning of LLM (GPT-x
PDF) How Does In-Context Learning Help Prompt Tuning?
Pre-training vs Fine-Tuning vs In-Context Learning of Large