Large language models (LLMs) like ChatGPT are impressive, but they sometimes stumble with complex reasoning. Think of it like having a brilliant conversationalist who struggles with multi-step instructions. They can generate creative text, but following a logical chain of thought is a different story. This is where ChatLogic comes in. This innovative framework acts like a logic tutor for LLMs, helping them master multi-step reasoning. How does it work? ChatLogic translates natural language into symbolic logic, a language that computers can easily manipulate. Imagine converting a complex word problem into a neat mathematical equation – that's essentially what ChatLogic does. This process allows LLMs to follow the reasoning steps more effectively, preventing them from getting lost or making logical errors. This framework integrates with existing LLMs, boosting their accuracy without extensive retraining. The researchers tested ChatLogic with various LLMs, including GPT-3.5, GPT-4, and Llama 2, using datasets designed to test multi-step reasoning. The results? ChatLogic consistently improved the accuracy of these models. While some high-performing models like GPT-4 initially showed strong results, their performance plateaued on harder problems, indicating the limit of current datasets. ChatLogic’s impact was most striking with less powerful LLMs, significantly boosting their reasoning abilities. This suggests that combining LLMs with logic-based systems is a promising way to make AI more reliable and accurate. ChatLogic, though still in its early stages, has the potential to revolutionize how we use LLMs, enabling them to tackle complex real-world problems requiring advanced reasoning. However, further research with more robust datasets and open-world scenarios is crucial for taking this framework to the next level.