Large language models (LLMs) like ChatGPT have taken the world by storm, generating human-quality text and even passing challenging exams. But how do these complex models actually decide which words to use? New research explores the surprising power of individual word choices in influencing an LLM's predictions. Researchers have developed a clever technique called NormXLogit to analyze which input words hold the most sway over an LLM's output. This method dives into the model's internal workings, examining how the initial 'embedding' of each word (its mathematical representation) interacts with the model's final prediction layer. The core idea is that the ‘norm’ (magnitude) of a word's initial embedding reflects its inherent importance, while the later layers show how this importance translates into influencing the final output. The results are fascinating: NormXLogit consistently pinpoints the most influential words in a sentence, outperforming traditional methods that rely on gradients (changes in model output relative to input). It even surpasses more complex, model-specific techniques in certain tasks. This breakthrough is not just theoretical. By understanding how LLMs prioritize words, we can enhance their transparency and build more reliable, trustworthy AI systems. Future research could extend NormXLogit to different model architectures and explore how combinations of words impact predictions, further unraveling the mysteries of how LLMs process and generate language.