Large language models (LLMs) are impressive, but they can also confidently generate incorrect information, making it crucial to know when they're likely to be wrong. Current methods for estimating LLM uncertainty rely on generating multiple outputs and comparing them, a computationally expensive process. New research explores a more efficient approach: G-NLL. This method leverages a core concept from probability scoring called the "zero-one score," which focuses on the likelihood of the *most* likely output. Instead of generating many different possible texts, G-NLL uses a simple "greedy decoding" strategy to produce a single, most probable output. The research shows that by calculating the negative log-likelihood of this single output, we can get a surprisingly accurate measure of the LLM's uncertainty. This simplified approach could make uncertainty estimation much more practical for real-world applications, enabling systems to flag potentially incorrect information generated by LLMs. While G-NLL currently doesn't factor in the semantic meaning of the generated text, future research will explore incorporating these nuances for even more precise uncertainty estimation. This shift towards single-output analysis holds promise for creating more trustworthy and reliable AI systems.