Large language models (LLMs) like ChatGPT seem to possess a vast amount of factual knowledge. But how do they actually store and access this information? A prevailing theory points to specific "knowledge neurons" within the model's neural network, acting like tiny memory banks holding individual facts. This "knowledge localization" theory suggests that each fact is neatly tucked away in a few designated neurons. However, new research challenges this neat and tidy view. The study, "Knowledge Localization: Mission Not Accomplished? Enter Query Localization!", reveals that many facts aren't localized to just a few neurons at all. Instead, the way an LLM accesses knowledge depends heavily on how the question is asked. The researchers propose a new "query localization" theory. They argue that the connection between a question and the neurons activated to answer it is dynamic and context-dependent. The attention mechanism within the LLM plays a crucial role, acting like a spotlight that focuses on the relevant parts of the model's vast network depending on the specific query. This research has significant implications for how we understand and interact with LLMs. It suggests that simply trying to pinpoint factual knowledge within the model's structure might be the wrong approach. Instead, we need to consider the complex interplay between the question, the attention mechanism, and the distributed nature of knowledge within the network. This shift in understanding could lead to more effective methods for modifying and updating the knowledge within LLMs, paving the way for more accurate and reliable AI systems.