Large language models (LLMs) like ChatGPT are impressive, but they have a tendency to "hallucinate"—making up facts and details. Why does this happen, and can it be fixed? New research suggests current methods for improving LLM accuracy, like using external knowledge bases, don't fully explain or solve the hallucination problem. In a surprising twist, researchers found that LLMs can easily memorize random strings of characters *without* harming their overall performance on other tasks. This shows LLMs have a huge capacity to memorize, but traditional training methods don't fully tap into this potential for factual accuracy. The study's authors propose a new technique called "memory tuning." Instead of focusing solely on general language understanding, memory tuning drills down on specific facts, training the model until it perfectly recalls them. It's like giving your AI a photographic memory for the facts that matter. However, there's a catch: perfectly memorizing billions of facts requires immense computing power. The researchers introduce Lamini-1, a new AI model designed to tackle this challenge by storing facts in a network of "memory experts." This setup makes factual recall much more efficient. The research sheds light on a major limitation of current LLM training. While training for general understanding is good for creative tasks, it isn't enough for precise factual recall. The team's work suggests a future where AI models can truly combine creativity with perfect factual accuracy—a critical step toward building truly trustworthy AI.