In the world of AI, the line between fact and fiction can get blurry. Large Language Models (LLMs) are powerful tools, but they can sometimes hallucinate or present false information as truth. This is especially problematic when dealing with events, where understanding the factuality of what happened is crucial. Imagine an AI summarizing news and mistaking a hypothetical scenario for a confirmed event – the consequences could be significant. That's why researchers at Tsinghua University have created MAVEN-FACT, a massive dataset designed to help AI better understand the factuality of events. MAVEN-FACT contains over 112,000 events labeled as factual, possible, or impossible, along with supporting evidence for each label. This makes it the largest dataset of its kind, providing a much-needed resource for training and evaluating AI models. What makes MAVEN-FACT unique is not just its size, but also its comprehensive annotations. It includes information on event types, arguments (who, what, where, when), and even the relationships between events. This richness allows for a deeper understanding of events and their factuality. Initial experiments show that MAVEN-FACT is challenging even for state-of-the-art models, highlighting the complexity of the task. However, researchers are optimistic that MAVEN-FACT can be a valuable tool for mitigating hallucinations and improving the reliability of LLMs. One promising application is question-answering systems. By providing the AI with factuality information, the system can avoid answering questions based on hypothetical or false events. This approach has shown significant improvements in accuracy, paving the way for more trustworthy and informative AI systems. MAVEN-FACT is not just a dataset; it represents a step towards more faithful and robust AI systems. As AI models continue to evolve, having a strong grasp on factuality will be crucial for ensuring accurate information and reliable decision-making.