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.

翻译：我们解决了自我学习大型语言模型的主要问题：即学习什么的问题。我们提出了一种自我学习大型语言模型框架，使大型语言模型能够通过对其自身幻觉的自我评估，独立学习先前未知的知识。利用幻觉评分，我们引入了“未知点”这一新概念，并提供了从外部和内部自动识别未知点的四种方法。这促进了专注于未知点知识差距的自我学习循环的创建，从而降低了幻觉评分。我们还开发了评估大型语言模型自我学习能力的度量标准。实验表明，经过微调或对齐的7B-Mistral模型能够相当良好地进行自我学习。我们的自我学习概念使得大型语言模型更新更加高效，并为知识交换开辟了新的视角，同时可能增强公众对人工智能的信任。