Large language models (LLMs) are impressive, but they sometimes 'hallucinate,' meaning they make things up. Researchers are tackling this problem, and a new paper introduces a promising approach. The challenge is to make LLMs not just fluent, but also accurate. Existing methods often involve simple fact-checking, labeling statements as either true or false. But what about more subtle errors, like misrepresenting relationships between entities or inventing details? This new research introduces PFME (Progressive Fine-grained Model Editor), a system designed to detect and correct these trickier hallucinations. PFME works in two main stages. First, a real-time fact retrieval module pulls in relevant information from trusted sources based on the text being generated. Then, a detection and editing module analyzes the text sentence by sentence, comparing it to the evidence and identifying the specific type of hallucination, if any. The system can then correct the error, flag it, or leave it unchanged, depending on the nature of the issue. Tested on challenging benchmarks, PFME outperforms existing methods in detecting fine-grained hallucinations, particularly when given access to external knowledge. It also substantially improves the factual accuracy of LLM-generated text. This represents a significant step forward in making AI more trustworthy. The modular design of PFME makes it adaptable to different LLMs and tasks. While there's still work to be done, this research demonstrates that more reliable and accurate AI is within reach. Future work will focus on refining the system and developing even more sophisticated detection and editing strategies.