In today's digital age, misinformation spreads like wildfire, making it more crucial than ever to distinguish fact from fiction. But can AI help us sift through the noise? New research explores how Large Language Models (LLMs) can be used to augment complex fact-checking, not just by verifying information but also by providing clear explanations. Researchers have created two new Chinese-language datasets, CHEF-EG and TrendFact, which pose complex fact-checking challenges involving numerical reasoning, logic, and common sense. These datasets cover topics from health and politics to social issues, pushing the boundaries of AI's fact-checking abilities. To tackle these challenges, the researchers have developed FactISR, a new framework that uses LLMs to iteratively revise and refine both the fact-checking process and the explanations generated. This "self-revision" process helps the AI identify and correct errors, leading to more accurate and transparent fact-checking. FactISR uses a single model to perform both fact verification and explanation generation, streamlining the process and making it more efficient. The iterative revision process works by feeding the generated explanations back into the system, allowing the AI to learn from its mistakes and improve its accuracy over time. Initial results are promising. FactISR significantly outperforms traditional methods, even surpassing the performance of powerful LLMs like GPT-4 in some cases. This suggests that the iterative revision approach is a key step toward building truly reliable AI fact-checkers. While this research focuses on Chinese-language fact-checking, the underlying principles and the FactISR framework can be applied to other languages as well. The next steps involve further refining the self-revision process and exploring how to make these AI fact-checkers more robust and resistant to manipulation. This research opens up exciting possibilities for using AI to combat misinformation and promote a more informed public discourse.