Into the Unknown: Self-Learning Large Language Models

Understand

We address the main problem of self-learning LLM: the question of what to learn.

• We propose a self-learning LLM framework that enables an LLM to independently learn previously unknown knowledge through self-assessment of their own hallucinations.
• We introduce a concept called Point in the Unknown (PiU) to identify atomic knowledge unknown to a model, along with four methods for automatic PiUs identification, facilitating the creation of a self-learning loop that focuses exclusively on the absorption of currently unknown knowledge into the model.
• Additionally, we developed evaluation metrics to gauge an LLM's self-learning capability.