Large language models (LLMs) are like sponges, soaking up vast amounts of knowledge. But what happens when they encounter conflicting information? Imagine telling an LLM that India has surpassed China as the most populous country, yet it still insists China holds the top spot. This "knowledge conflict" arises from the clash between an LLM's internalized, pre-trained knowledge and the fresh facts presented in context. Researchers have developed a clever solution called IRCAN, which stands for Identifying and Reweighting Context-Aware Neurons. It works by pinpointing and boosting the specific neurons within the LLM that are responsible for processing contextual clues. It’s like giving these neurons a megaphone, ensuring that the LLM pays more attention to the immediate context instead of clinging to outdated internal knowledge. IRCAN's effectiveness has been demonstrated across various tasks and LLM architectures, showing remarkable improvements in resolving these knowledge conflicts. For instance, on certain tests, IRCAN boosted the accuracy of some LLMs by over 100% when dealing with contradictory information. The beauty of IRCAN is its plug-and-play nature, meaning it can be easily integrated into existing LLM systems. Moreover, it complements other existing techniques like context-aware decoding (CAD), enhancing their effectiveness. This research offers a significant leap toward making LLMs more context-sensitive and reliable. By fine-tuning how LLMs process and prioritize information, IRCAN moves us closer to AI that can accurately navigate complex and ever-evolving knowledge landscapes. While the current research focuses on specific knowledge conflict scenarios, future explorations into long-context tasks and retrieval augmented generation (RAG) hold exciting potential for even more sophisticated and contextually aware LLMs.