Large language models (LLMs) are impressive, but they can get things wrong. Researchers are trying to find ways to "edit" an LLM's knowledge, correcting its factual errors without expensive retraining. However, this "knowledge editing" is proving trickier than it sounds. A new research survey reveals some serious pitfalls in current editing methods. One problem is that edits don't always generalize well. Correcting one fact might not fix related errors, and LLMs may struggle to apply edited knowledge in new situations. Another issue is unwanted side effects. Tweaking one piece of information can inadvertently corrupt unrelated knowledge or even harm the model’s overall performance. Imagine fixing a typo and accidentally deleting a whole paragraph! This highlights the complexity of how LLMs store information. It isn’t a simple database of facts. Instead, knowledge is distributed throughout the model's neural network. Current editing methods are like performing brain surgery with blunt instruments. Researchers are working on ways to improve knowledge editing methods, such as combining external knowledge sources and enhancing internal models. The goal is to make these edits more precise and predictable, ensuring that LLMs can be corrected without unintended damage. The challenge isn’t just about fixing facts. It’s about developing a better understanding of how LLMs organize and process knowledge, enabling smoother, more efficient integration of new information in the future.