Large language models (LLMs) like ChatGPT are impressive, but they sometimes "hallucinate," making up facts. How can we make sure they're telling the truth? New research explores a clever approach: setting up AI debates. Imagine a courtroom where different AI agents act as trustful witnesses, skeptical lawyers, and a neutral judge. That's the idea behind a new "Markov Chain-based Multi-agent Debate Framework." Researchers extract specific claims from an LLM's response and then have these AI agents debate their accuracy using evidence. One agent tries to defend the LLM's output, another challenges it, and a third acts as the judge, weighing the arguments. This back-and-forth continues, like a Markov chain, with each step influenced by the last, until a verdict is reached. This process makes the fact-checking dynamic, mimicking how humans argue and reason. Experiments on question-answering, summarization, and dialogue tasks show promising results, suggesting this framework can boost the accuracy of LLM outputs. While there are challenges, such as the cost of running multiple AI agents and the potential for repetitive arguments, this multi-agent debate approach offers an intriguing path toward more trustworthy and reliable AI.