Large language models (LLMs) are impressive, but they sometimes make mistakes, especially with complex reasoning tasks. Think of it like a brilliant student who works incredibly fast but occasionally skips steps or misinterprets the question. Researchers have noticed that these errors often stem from the way LLMs generate text—predicting the next word without a true understanding of the overall process. A new approach called LLM2 aims to give LLMs the ability to double-check their work, much like a human might review and revise their initial thoughts. This is inspired by the dual-process theory of human cognition, which suggests our thinking involves two systems: System 1, responsible for fast, intuitive thinking, and System 2, responsible for slower, more deliberate reasoning. LLM2 works by adding a 'verifier' to the existing LLM. The LLM (System 1) generates potential answers, while the verifier (System 2) acts as a critical editor, providing feedback on each step of the process. This verifier is trained to recognize desirable and undesirable patterns in the LLM's output, helping it identify mistakes and guide the LLM towards a more accurate solution. Tests on mathematical reasoning problems show LLM2 significantly improves accuracy. For example, one version of the LLM saw an accuracy jump of 7.5% on a challenging math dataset. Interestingly, the verifier learns to identify these errors without explicit human guidance. Instead, it utilizes a clever strategy that analyzes the potential impact of each word the LLM generates. By examining how different word choices affect the final answer, the verifier learns to recognize patterns indicative of errors. This development has exciting implications. By giving LLMs the ability to self-correct, we can improve the reliability and trustworthiness of AI-generated content, paving the way for more complex and nuanced applications. While currently tested on math problems, this method could potentially extend to other areas like code generation or commonsense reasoning, making LLMs even more powerful tools in the future. However, challenges remain, particularly in open-ended tasks like creative writing where the concept of a 'correct' answer is less clear. Future research will likely focus on adapting this method for broader applications and addressing the inherent difficulties in verifying subjective or creative content.