I like to think of an LLM as a literal human. Not sure if it's the best analogy.
Fine tuning = Adding years of experience, in a set environment. e.g. Raise them in a home that only speaks in old english, learn pig latin, send them to a bootcamp.
Embedding = Giving them a book to reference information.
Just like a human, memory might fade a bit through the years but old habits die hard. You might not perfectly recollect what you learned years ago, but you still get the general idea, and if you took a class on the referenced book you'll be better at relaying information from it.
Edit: Asked ChatGPT to create the analogy.
A language model is like an intelligent person.
- Pre-training is their broad education and general knowledge.
- Fine-tuning is their years of specialized experience in a specific field.
- Embedding is like giving them a comprehensive book on a particular subject.
Just as a person gains knowledge, expertise, and specialized resources, the language model develops its understanding and performance through pre-training, fine-tuning, and embedding.
Fine tuning = Adding years of experience, in a set environment. e.g. Raise them in a home that only speaks in old english, learn pig latin, send them to a bootcamp.
Embedding = Giving them a book to reference information.
Just like a human, memory might fade a bit through the years but old habits die hard. You might not perfectly recollect what you learned years ago, but you still get the general idea, and if you took a class on the referenced book you'll be better at relaying information from it.
Edit: Asked ChatGPT to create the analogy.
A language model is like an intelligent person.
- Pre-training is their broad education and general knowledge.
- Fine-tuning is their years of specialized experience in a specific field.
- Embedding is like giving them a comprehensive book on a particular subject.
Just as a person gains knowledge, expertise, and specialized resources, the language model develops its understanding and performance through pre-training, fine-tuning, and embedding.