Large Language Models (LLMs) are impressive, but they have a problem: they sometimes 'hallucinate,' generating believable yet false information. This poses a significant challenge to their widespread adoption, particularly in fields demanding accuracy. Imagine an AI lawyer citing nonexistent cases or a medical chatbot giving dangerous health advice! This is why researchers are exploring Knowledge Graphs (KGs) as a potential solution. KGs are structured databases of facts, representing entities and their relationships, that can provide a factual grounding for LLMs. By connecting LLMs to this external source of truth, they hope to reduce hallucinations and boost reliability. This involves several approaches, including incorporating KGs during LLM training, dynamically injecting KG information during the generation process, and even retrofitting the LLM's output by checking it against a KG after generation. While early research is promising, significant challenges remain. Current methods often rely on complex prompting or multi-stage pipelines that are themselves prone to errors. Furthermore, evaluating the effectiveness of these methods is tricky. How do you comprehensively measure factuality, especially across different languages and tasks? Creating robust, multilingual KGs and developing more sophisticated integration techniques are crucial next steps. The ability to effectively combat hallucinations will be a key factor in unlocking the full potential of LLMs, paving the way for their trustworthy application in critical areas like healthcare, law, and education.