Large language models (LLMs) like ChatGPT are impressive, but they sometimes make things up—a problem known as "hallucination." It's hard to tell when an LLM is confident in its answer versus just guessing. New research from Stanford and IBM introduces a clever approach called "Graph Uncertainty" to tackle this. Imagine representing the relationship between an LLM's output and the facts within it as a network. This research does just that, creating a graph where connections show how different parts of the text support each other. By analyzing the connections in this graph, researchers can pinpoint which parts of the text are likely to be true and which are more uncertain. This method goes beyond simply counting how often an LLM repeats the same information. It digs deeper into the *relationships* between different pieces of information, using a concept called "closeness centrality." This basically measures how central a piece of information is within the overall network. The more central, the more likely it is to be true. The results are promising. This graph-based approach is significantly better at identifying unreliable information compared to existing methods. It even helps LLMs generate more factual text by filtering out uncertain claims before they're presented to the user. This research is a big step toward making LLMs more trustworthy. Imagine a future where you can rely on AI for accurate information, knowing that it can identify and flag its own uncertainties. While challenges remain, such as the computational cost of building these graphs, this work paves the way for more reliable and transparent AI systems.