Can AI truly understand the nuances of events happening in the world? Researchers are constantly pushing the boundaries of what Large Language Models (LLMs) like GPT-4 can achieve. One area ripe for improvement is how these models detect and categorize events within text. A new study has explored an innovative technique to enhance both the accuracy and the confidence of GPT-4 in few-shot event detection. Imagine trying to teach a computer to identify specific events, like arrests or disease outbreaks, from news articles. Giving it just a few examples isn't enough for it to confidently discern complex situations. This is where "License & Opportunity" (L&O) comes in. This method encourages GPT-4 not only to make educated guesses when unsure but also to explain its reasoning and quantify its uncertainty. By granting the model this license to speculate and the opportunity to articulate its confidence, researchers observed significant improvements. The L&O approach enhances GPT-4's ability to pinpoint events with higher accuracy and provides a more reliable confidence score. The model not only identifies events like “Judicial-Convict” or “Disease-Outbreak” more effectively but also signals how sure it is about its classifications. This added layer of transparency is invaluable, especially when dealing with sensitive information. Furthermore, the research reveals fascinating insights into GPT-4's inner workings. It appears the model is capable of expressing uncertainty, but needs explicit encouragement to do so. This highlights the importance of prompting strategies in unlocking the full potential of LLMs. While the study focused on English-language news and specific event types, the implications are far-reaching. Improving confidence estimation in LLMs is critical for building trustworthy AI systems. L&O paves the way for more robust event detection and opens new avenues for using LLMs in critical real-world applications. From automating information extraction to assisting human analysts, L&O could be a game-changer in how we process and understand the ever-growing flood of textual data.