Large Language Models (LLMs) have showcased impressive reasoning
capabilities, particularly when guided by specifically designed prompts in
complex reasoning tasks such as math word problems. These models typically
solve tasks using a chain-of-thought approach, which not only bolsters their
reasoning abilities but also provides valuable insights into their
problem-solving process. However, there is still significant room for enhancing
the reasoning abilities of LLMs. Some studies suggest that the integration of
an LLM output verifier can boost reasoning accuracy without necessitating
additional model training. In this paper, we follow these studies and introduce
a novel graph-based method to further augment the reasoning capabilities of
LLMs. We posit that multiple solutions to a reasoning task, generated by an
LLM, can be represented as a reasoning graph due to the logical connections
between intermediate steps from different reasoning paths. Therefore, we
propose the Reasoning Graph Verifier (RGV) to analyze and verify the solutions
generated by LLMs. By evaluating these graphs, models can yield more accurate
and reliable results.Our experimental results show that our graph-based
verification method not only significantly enhances the reasoning abilities of
LLMs but also outperforms existing verifier methods in terms of improving these
models' reasoning performance.

我们引入了一种基于图的方法来增强大型语言模型的推理能力，通过分析和验证由 LLMs 生成的解决方案，我们的实验结果表明，我们的基于图的验证方法不仅显著提高了 LLMs 的推理能力，而且在提高这些模型的推理性能方面优于现有的验证程序。