Large language models (LLMs) are increasingly used to generate long-form text, from articles to creative writing. But how can we trust what they say? A new research paper tackles this question by exploring how to quantify uncertainty in factual claims made by LLMs. The study focuses on two key aspects: ensuring individual claims are accurate and guaranteeing the reliability of the entire output. Researchers examined these problems through the lens of "multi-group uncertainty quantification." This approach accounts for potential biases by examining accuracy across various subgroups within the data. For example, an LLM might be more accurate when writing about well-known historical figures than about less-documented contemporary individuals. Using biography generation as a test case, the research revealed that incorporating group features, such as nationality or profession, leads to significantly better uncertainty estimates. The results suggest that even without specific fairness requirements, considering these group attributes can substantially enhance the reliability of LLM-generated factual content. This work introduces new methods for both "calibrating" individual claim accuracy and providing "conformal predictions" for the overall truthfulness of generated text. The findings have important implications for building more trustworthy and robust AI systems, paving the way for applications where factual accuracy is paramount. While promising, challenges remain. Current methods rely on automated fact-checking against Wikipedia, a useful but imperfect proxy for real-world accuracy. Future research might explore alternative approaches to validation, potentially involving human feedback or more comprehensive knowledge bases. Furthermore, the study primarily focused on biographies. Applying these techniques to other text generation domains, such as news articles or scientific reports, could reveal further insights and challenges. This research marks a significant step toward addressing the critical issue of factuality in LLM outputs, fostering greater trust and responsibility in the age of AI-generated content.