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Pre-training vs Fine-Tuning vs In-Context Learning of Large

By A Mystery Man Writer

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.

The overview of our pre-training and fine-tuning framework.

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