Large language models (LLMs) like ChatGPT have taken the world by storm. They can write poems, answer questions, and even code, but they also have a tendency to 'hallucinate,' confidently stating things that are completely made up. This poses a major problem for real-world applications where reliability is key. New research dives into this issue of 'uncertainty,' exploring ways to measure how sure an LLM is about its own predictions. Researchers have developed a clever technique called Semantically Diverse Language Generation (SDLG). Imagine asking an LLM a question, and instead of getting one answer, you get several different but plausible responses. By examining the range of answers, SDLG can measure how much the LLM wavers in its understanding. This is akin to 'stress-testing' the model, revealing its hidden doubts. This approach has shown promising results, outperforming existing methods in identifying correct and incorrect answers on various question-answering datasets. The advantage of SDLG lies not just in its accuracy but also its efficiency. It's like a detective asking targeted questions to get to the truth quickly, avoiding the computational cost of generating numerous, often repetitive, responses. SDLG strategically alters key parts of an initial answer and observes how the LLM adapts, providing a sharper lens into its confidence levels. This method is more computationally efficient and less random than previous approaches, avoiding the need for extensive hyperparameter tuning. This new research is an important step towards making LLMs more trustworthy. While challenges remain, especially in handling longer, more nuanced text formats, SDLG offers a promising path towards creating AI that knows when it doesn't know something—a critical trait for building reliable and responsible AI systems.