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. Using the hallucination score, we introduce a new
concept of Points in The Unknown (PiUs), along with one extrinsic and three
intrinsic methods for automatic PiUs identification. It facilitates the
creation of a self-learning loop that focuses exclusively on the knowledge gap
in Points in The Unknown, resulting in a reduced hallucination score. We also
developed evaluation metrics for gauging an LLM's self-learning capability. Our
experiments revealed that 7B-Mistral models that have been finetuned or aligned
are capable of self-learning considerably well. Our self-learning concept
allows more efficient LLM updates and opens new perspectives for knowledge
exchange. It may also increase public trust in AI.

我们提出了一种自学习 LLM 框架，通过对自身幻觉的自我评估，使 LLM 能够独立学习先前未知的知识。通过幻觉评分，我们引入了一个新概念 Points in The Unknown（PiUs），以及一种外在和三种内在的自动 PiUs 识别方法，从而在 Points in The Unknown 的知识差距中创建一个自学习循环，从而减少幻觉评分。我们还开发了评估指标来衡量 LLM 的自学习能力。我们的实验表明，经过微调或调整的 7B-Mistral 模型具有相当好的自学习能力。我们的自学习概念可以实现更高效的 LLM 更新，并为知识交流开辟新的视角，这也可能增加公众对 AI 的信任。