Fine-tuning is a machine learning technique in which a pre-trained model that has already learned from a large dataset is trained on a smaller, domain-specific dataset to tailor its knowledge to a specific task or application. Fine-tuning is used in OpenAI's ChatGPT to improve model control and suitability for specific use scenarios.
Here's how the fine-tuning process generally works:
Pre-training: ChatGPT goes through an early step called pre-training, in which it is exposed to a large and diverse dataset from the internet. During this phase, the model learns general language patterns, syntax, and world knowledge.
Custom Dataset Creation: Following pre-training, OpenAI generates a customized dataset for fine-tuning. This dataset has been carefully vetted, and it may contain instances relevant to the target application or domain. It enables the model to specialize and apply its knowledge to certain themes or limitations.
Fine-tuning Process: Fine-tuning involves training the model on a bespoke dataset while keeping certain tasks, recommendations, or limitations in mind.
The training procedure consists of modifying the model's parameters based on the new dataset while keeping the knowledge gained during pre-training.
The goal is to make the model more controllable, safe, and responsive to user expectations.
Safety Measures: OpenAI integrates safety mechanisms during fine-tuning to reduce the risks associated with biases, dangerous content production, and other unwanted behaviors.
OpenAI attempts to improve the model's behavior and ensure it meets ethical standards by fine-tuning carefully created datasets and implementing guidelines.
Iterative Process: Fine-tuning is frequently an iterative process that includes several rounds of modifying hyperparameters, improving the custom dataset, and assessing the model's performance.
Feedback from users and external reviews can help to improve the fine-tuning process.
Fine-tuning in ChatGPT helps address some of the issues associated with big language models, such as creating unsuitable content or responses. It enables OpenAI to steer and shape the model's behavior, allowing for more responsible and controlled use in a variety of applications such as user interactions and content development.
