Large language models (LLMs) are impressive text generators, but they sometimes struggle to stay true to given information. Think of it like a student with lots of general knowledge who sometimes forgets the specific details in their textbook. Researchers have discovered a clever technique called "FastMem" that boosts an LLM's ability to remember and prioritize the information it's given. Imagine giving the student a photographic memory just for their current assignment. FastMem works by focusing the LLM's attention on the immediate task, essentially reducing its "perplexity" or uncertainty about the given context. Instead of retraining the entire model, which is time-consuming and resource-intensive, FastMem cleverly updates just a small part—the last 'Feed-Forward Network' module. This targeted approach allows the LLM to quickly internalize the provided information without forgetting everything else it knows. Tests show that FastMem significantly enhances an LLM’s performance in tasks like question answering and text summarization. It's particularly useful when the given text contradicts the LLM’s existing knowledge or when the instructions are a bit counterintuitive. In essence, FastMem unlocks the true power of LLMs by ensuring they stay laser-focused on the context, providing more accurate and faithful results. This technique represents a significant step towards resolving the so-called context-awareness challenge in LLMs, and it has a wide range of applications, from accurate summarization tools to more robust conversational AI.