Large language models (LLMs) are like the promising new intern at a news agency—brilliant, eager, and occasionally prone to making stuff up. They can write long, impressive articles on any topic, but sometimes their facts are… alternative. This "hallucination" problem poses a serious challenge if LLMs are to become our next-generation information providers. Researchers are tackling this challenge head-on with techniques like FactAlign, a new framework designed to make LLM output more truthful without sacrificing helpfulness. Imagine giving that eager intern a super-powered fact-checker. That’s FactAlign. It works by breaking down an LLM’s long-form response into individual sentences, then rigorously checking each sentence against a reliable knowledge base like Wikipedia. This fine-grained fact-checking is key to identifying and correcting subtle errors that might escape broader checks. It’s not just about saying “This whole article is mostly true”; it’s about pinpointing exactly where an LLM goes off the rails. The clever part is that FactAlign uses these individual fact checks to train the LLM. Each identified error becomes a learning opportunity, guiding the model towards more accurate future responses. Researchers found that FactAlign significantly improves the truthfulness of LLM outputs, especially in longer responses where errors are more likely to creep in. This research has exciting real-world implications. More truthful LLMs could revolutionize everything from search engines to customer service chatbots, providing reliable information and assistance. However, challenges remain. Even with meticulous fact-checking, LLMs can still sometimes generate convincing but false information. Future research might explore ways to make LLMs more aware of their own limitations, perhaps adding a "confidence score" to their output to warn users when a fact is less certain. FactAlign is a big step toward making LLMs more trustworthy, moving us closer to a future where AI can be a reliable source of knowledge and insight.