Large language models (LLMs) are impressive, but they can sometimes generate incorrect or nonsensical text, a phenomenon known as 'hallucination.' Knowing when an LLM is uncertain about its response is crucial, especially in fields like medicine or law. Current methods for measuring uncertainty often fall short because they don't fully grasp the *meaning* behind the words. A new technique called Kernel Language Entropy (KLE) tackles this problem by examining the semantic similarity between different LLM-generated answers. Imagine asking an LLM a question and getting slightly different responses each time. Instead of just counting how many different words are used, KLE looks at how closely related the *meanings* of those responses are. If the answers all convey similar ideas, even if phrased differently, KLE recognizes higher certainty. This nuanced approach allows KLE to identify subtle uncertainties that other methods miss. Researchers tested KLE on various LLMs and question-answering datasets, from general knowledge to complex math problems. The results? KLE consistently outperformed existing uncertainty measures, proving its potential to make LLMs more reliable and trustworthy. This breakthrough could pave the way for safer and more responsible use of LLMs in critical applications, helping us know when to trust AI and when to seek human expertise.