Large language models (LLMs) like ChatGPT are impressive, but they sometimes “hallucinate,” making up facts that sound convincing. Researchers are exploring clever ways to combat this, and one intriguing method involves making LLMs debate each other. Imagine a mini digital courtroom where some AIs are assigned to argue for the truth while others try to convincingly present false information. This isn't just a theoretical exercise. A recent study pitted multiple GPT-4o-Mini models against each other, using questions from the TruthfulQA dataset. One AI was instructed to be the “saboteur,” crafting plausible but false answers. The others had to stick to the facts. A final “moderator” AI then judged the debate and chose the winning answer. The results? The debating AIs, on average, achieved 78.72% accuracy, a significant jump from the baseline 61.94% accuracy without the debate setup. This suggests that challenging LLMs with conflicting information can force them to justify their reasoning and improve their ability to discern truth from falsehood. However, it wasn't a perfect victory. The AIs still struggled with nuanced topics like superstitions and the paranormal, showing they're more easily swayed by misinformation in areas requiring contextual understanding. Interestingly, they excelled in categories like history and weather, where facts are more clear-cut. This research highlights the potential of using inter-model interaction to combat hallucinations. By creating a sort of “cognitive friction” through debate, we might be able to train more robust and reliable LLMs. While more research is needed to explore different debate formats and expand the types of misinformation tested, this study offers a promising glimpse into the future of making LLMs more truthful and trustworthy.