What does "Learning Over Time" mean?
Table of Contents
Learning over time refers to the process by which models, like large language models, adapt and improve their understanding as they encounter new information. These models are trained using vast amounts of text, which helps them gather knowledge about various topics.
Types of Knowledge
Models can store different kinds of information, especially when it comes to facts that may change over time, such as names of people or events. Understanding how and where this information is stored within the model is important for making them more effective in learning new facts without forgetting what they've previously learned.
Improving Continuous Learning
When models receive new information, it's important for them to focus on the right parts of their framework. By doing so, they can better absorb updates and maintain their past knowledge. This approach helps prevent them from mixing up old and new information, which can happen if they are not directed properly.
Dynamic Frameworks
To make the most out of language models, a flexible system can be developed to choose the best model for each specific task. This system takes into account factors like the type of query and the resources available. By doing this, users can achieve better results at a lower cost, making it easier to use language models effectively.