Types of Fine Tuning

Some definitions

Supervised Fine-Tuning

Supervised is when you tell the model what the answer is - the most basic type

• Definition: Supervised fine-tuning involves training a model on a labeled dataset, where each piece of data (like a sentence or image) is paired with a correct answer or label.

• Process: The model learns by comparing its predictions to these correct answers and adjusting itself to improve accuracy.

• Example: Training a language model to translate sentences by using a dataset where each English sentence is paired with its French translation.

Unsupervised Fine-Tuning

• Definition: Unsupervised fine-tuning does not use labeled data. Instead, the model learns patterns or features from the data itself without explicit guidance on what is correct.

• Process: The model tries to find structure in the data, like grouping similar things together or predicting parts of the data based on other parts.

• Example: Training a model to group news articles into topics without knowing in advance what the topics are.

Self-Supervised Fine-Tuning

The answer comes naturally from the task

• Definition: Self-supervised learning is a hybrid approach that falls between supervised and unsupervised learning. It involves creating a pseudo-labeled dataset from the unlabeled data.

• Process: The model itself generates labels from the data (hence "self-supervised") and then trains on these labels. Often, this involves masking parts of the data and training the model to predict them.

• Example: A language model is given sentences with some words missing and learns to predict the missing words.

Summary

• Supervised: Needs labeled data and learns to predict correct answers.

• Unsupervised: No labels are involved, and the model learns patterns or structures from the data itself.

• Self-Supervised: Generates its own labels from the data and learns like in supervised learning, but without needing external labels.

Each of these methods has its own strengths and is suitable for different types of problems and datasets.

Supervised learning is very direct and effective when labeled data is available, unsupervised learning is useful for uncovering hidden structures in data, and self-supervised learning offers a balance by exploiting unlabeled data in a structured way.