Large language models (LLMs) are impressive, but they can sometimes generate incorrect or nonsensical outputs, a phenomenon known as "hallucination." How can we tell when an LLM is confident in its response versus just making things up? Researchers are tackling this crucial problem of uncertainty quantification, and a new method called "semantic density" is showing promising results. Imagine an LLM generating multiple answers to the same question. Instead of just looking at the words themselves, semantic density analyzes the *meaning* of these responses. It creates a "semantic space" where similar answers cluster together. The more densely packed the responses are in this space, the higher the confidence. This approach is like gauging the consensus among experts—if they all agree, you're more likely to trust their judgment. Semantic density has several advantages. It doesn't require retraining the LLM, works across different types of tasks, and provides a confidence score for each individual response. In tests across several leading LLMs and question-answering datasets, semantic density outperformed existing uncertainty methods. This research is a significant step towards making LLMs more trustworthy. By understanding when an LLM is uncertain, we can use them more responsibly in critical applications like healthcare and finance. Future research could explore even better ways to represent meaning and measure semantic similarity, further refining our ability to assess AI confidence.