GraphReason：通过基于图的验证方法增强大型语言模型的推理能力
GraphReason: Enhancing Reasoning Capabilities of Large Language Models through A Graph-Based Verification Approach
AI总结：
本文提出了一种基于图的验证方法GraphReason，通过将LLM生成的多个推理解决方案表示为推理图并进行分析验证，显著提升了大型语言模型的推理能力，且优于现有验证器方法。
摘要：
大型语言模型（LLMs）展现出了令人印象深刻的推理能力，特别是在数学应用题等复杂推理任务中，当受到专门设计的提示引导时尤为突出。这些模型通常采用思维链方法来解决任务，这不仅增强了它们的推理能力，还为其问题解决过程提供了有价值的见解。然而，LLMs的推理能力仍有很大的提升空间。一些研究表明，集成LLM输出验证器可以在无需额外模型训练的情况下提高推理准确性。在本文中，我们遵循这些研究，引入了一种新颖的基于图的方法来进一步增强LLMs的推理能力。我们认为，由LLM生成的推理任务的多个解决方案，由于来自不同推理路径的中间步骤之间存在逻辑联系，可以表示为推理图。因此，我们提出了Reasoning Graph Verifier（GraphReason）来分析和验证LLMs生成的解决方案。通过评估这些图，模型可以产生更准确和可靠的结果。我们的实验结果表明，我们基于图的验证方法不仅显著增强了LLMs的推理能力，而且在提升这些模型推理性能方面优于现有的验证器方法。
英文摘要：
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 (GraphReason) 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.
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