Language models linearly represent truth or falsehood in factual statements and have a unique structure that can be extracted using mass-mean probing, a novel technique that generalizes better than traditional probing methods. The paper presents evidence of this structure and shows how it can be used to improve the reliability of language models. The authors' goal is to develop a way for humans to access what AI systems know about truth and falsehood, which would enable more accurate evaluations of their outputs. The research has implications for the development of more reliable LLMs and addressing the scalable oversight problem as AI systems become more capable.