Large language models (LLMs) are impressive, but they have a problem: they sometimes make things up. This "hallucination" is a major hurdle in building truly helpful and reliable AI. New research explores this issue in a paper titled "FLAME: Factuality-Aware Alignment for Large Language Models." The core problem is that current training methods, while making LLMs better at following instructions, can actually make them *less* factual. Imagine training an AI assistant on information it's never encountered before—it might start filling in gaps with guesses, leading to inaccuracies. The researchers found that even using seemingly better training data, like text from retrieval-augmented LLMs (which are generally more factual), can backfire and increase these hallucinations. So, how do we fix this? The FLAME approach tackles this by being "factuality-aware" during training. It identifies instructions that require factual responses (like "Tell me about the American Revolution") and uses a clever trick: it generates training data from the LLM's *own* knowledge base. This prevents it from learning potentially incorrect information from external sources. Furthermore, FLAME uses a separate reward system during reinforcement learning to specifically encourage factual responses. The results are promising. FLAME-trained LLMs are significantly more factual without losing their ability to follow instructions. This is a big step towards building AI that we can truly trust. However, the challenge isn't fully solved. AI models still struggle with nuanced instructions and require more sophisticated methods to understand when and how to prioritize factuality. The future of AI depends on cracking this nut, and research like FLAME provides a crucial path forward.