Into the Unknown: Self-Learning Large Language Models
<|reference_start|>Into the Unknown: Self-Learning Large Language Models: 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. Our experiments revealed that LLMs with at least 3B parameters that have undergone some instruction training would be able to perform self-learning well. We further proved the effectiveness of self-learning by comparing the performance of a model that has undergone self-learning to a model that has not. Our self-learning concept allows more efficient LLM updates and opens new perspectives for LLM knowledge exchange.<|reference_end|>
@article{ferdinan2024into,
    title={Into the Unknown: Self-Learning Large Language Models},
    author={Teddy Ferdinan, Jan Koco'n, Przemys{l}aw Kazienko},
    journal={arXiv preprint arXiv:2402.09147},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.09147},
    primaryClass={cs.AI}
}