Large language models (LLMs) are impressive, but they can still 'hallucinate,' or make things up. Researchers are trying to understand *why* this happens. One approach is to search within the model's inner workings for a 'truthfulness hyperplane.' This hyperplane, if it exists, acts like a filter, separating true statements from false ones. Previous research has shown that hyperplanes trained on one dataset don't work well on others. They 'overfit' – becoming too specialized. In a new paper, “On the Universal Truthfulness Hyperplane Inside LLMs,” researchers explored whether a *universal* truthfulness hyperplane exists—one that works across different topics and tasks. To find out, they trained a hyperplane using a massive collection of 40+ datasets. They then tested how well it generalized to new, unseen data. The results were encouraging. The diverse training significantly improved the hyperplane’s ability to detect hallucinations. Interestingly, it didn't take much data from each individual dataset to achieve good results, suggesting that a universal truthfulness measure is within reach. This research offers promising insights into how LLMs represent truth. A universal hyperplane could make LLMs more reliable and trustworthy by helping them distinguish fact from fiction. Further research will focus on refining these techniques and developing practical applications to improve AI’s accuracy and reduce hallucinations.