Large language models (LLMs) are impressive, but they sometimes generate false information—a problem known as "hallucination." Ensuring these models produce reliable content is crucial for building trust and making them truly useful. How can we teach AI to be more truthful? Researchers have developed a clever new method called HaloScope that allows LLMs to essentially fact-check themselves using their own output. Instead of relying on scarce, manually labeled data to identify hallucinations, HaloScope taps into the vast amount of unlabeled text generated by LLMs "in the wild." Think of all the text produced in chat applications or when a language model like GPT responds to user prompts—a mix of accurate and potentially false statements. HaloScope uses an innovative technique to separate the true from the false. It analyzes the model’s internal representations of language, looking for patterns that reveal whether a statement is likely a hallucination. By identifying a “hallucination subspace” within these representations, HaloScope can estimate the membership of an unlabeled text sample, tagging it as potentially true or false. This automated labeling process creates a training dataset for a truthfulness classifier. Essentially, HaloScope trains a separate component to recognize and flag potential hallucinations, without needing humans to label examples. Experiments show HaloScope is remarkably effective. It significantly outperforms existing methods in detecting false statements across several datasets and different LLMs, even approaching the accuracy of methods trained on manually labeled data. This approach addresses the critical bottleneck of data scarcity and provides a scalable, efficient way to improve the reliability of LLMs. While distributional shifts between training and test data remain a challenge, HaloScope opens exciting new avenues for building more truthful AI systems.