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Fine Tuning = Output Embeddings = Input Fine-tuning is like a chef modifying a general pizza recipe to perfect a specific pizza, such as Neapolitan. This cust

by swalsh 3y ago

Fine Tuning = Output Embeddings = Input Fine-tuning is like a chef modifying a general pizza recipe to perfect a specific pizza, such as Neapolitan. This customization optimizes the result. In AI, fine-tuning adjusts a pre-existing model to perform better on a specific task. Embeddings are like categorizing ingredients based on properties. They represent inputs so that similar inputs have similar representations. For instance, 'dog' and 'puppy' in an AI model have similar meanings. Like ingredients in a pizza, embeddings help the model understand and interpret the inputs. So, fine-tuning is about improving the model's performance, while embeddings help the model comprehend its inputs. It turns out, you can search a vector space of embeddings to find similar embeddings. If I turned my above post into 2 embeddings, and you searched for "golden retreiver" though neither paragraph has that exact phrase, the model should know a golden retreiver is most similar to the second paragraph that compares puppy to dog.

SparkyMcUnicorn 3y ago
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.

by swalsh 3y ago

SparkyMcUnicorn 3y ago

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.