Large language models (LLMs) have shown remarkable reasoning abilities, especially with chain-of-thought prompting, where they generate intermediate steps before arriving at a final answer. However, these reasoning chains can sometimes be flawed, leading to correct answers through incorrect logic. Think of it like a student getting the right answer on a math test but showing the wrong work—the answer might be right by chance, but the underlying reasoning is flawed. Researchers are exploring ways to make LLMs more reliable reasoners by verifying each step in their thought process. A new research paper introduces a general-purpose verification method for chain-of-thought prompting. This method uses a set of 'verifiers' that check each step for relevance, mathematical accuracy, and logical consistency. Imagine these verifiers as automated fact-checkers, ensuring that each step in the LLM's reasoning makes sense and builds upon the previous ones. In addition to these checks, the method also uses the perplexity of each step as a measure of how likely it is to be correct. The researchers tested this method on various reasoning tasks, including math word problems, commonsense reasoning, and symbolic manipulation. They found that using these verifiers significantly improved the accuracy of the LLM's reasoning, outperforming both random sampling and selecting the lowest-perplexity chain. This suggests that the verifiers provide valuable information beyond what's captured by perplexity alone. The results are promising, showing that LLMs can, to some extent, identify their own mistakes. This opens up exciting possibilities for creating more reliable and robust AI systems that can explain their reasoning in a transparent and verifiable way. However, there are still challenges to overcome. The current implementation of the verifiers relies on LLMs themselves, which can be computationally expensive. Further research is needed to develop more efficient verification methods and to explore how these techniques can be applied to a wider range of tasks and domains. This research is a step towards building AI systems that not only generate answers but also provide clear, verifiable, and trustworthy explanations for how they arrived at those answers.