Large language models (LLMs) have achieved remarkable performance in natural language understanding and generation tasks. However, they often suffer from limitations such as difficulty in incorporating new knowledge, generating hallucinations, and explaining their reasoning process. To address these challenges, we propose a novel prompting pipeline, named \method, that leverages knowledge graphs (KGs) to enhance LLMs' inference and transparency. Our method enables LLMs to comprehend KG inputs and infer with a combination of implicit and external knowledge. Moreover, our method elicits the mind map of LLMs, which reveals their reasoning pathways based on the ontology of knowledge. We evaluate our method on diverse question \& answering tasks, especially in medical domains, and show significant improvements over baselines. We also introduce a new hallucination evaluation benchmark and analyze the effects of different components of our method. Our results demonstrate the effectiveness and robustness of our method in merging knowledge from LLMs and KGs for combined inference. To reproduce our results and extend the framework further, we make our codebase available at https://github.com/wyl-willing/MindMap.

翻译：大型语言模型（LLMs）在自然语言理解与生成任务中取得了显著性能，但仍面临知识更新困难、生成幻觉以及推理过程难以解释等局限。为解决这些挑战，我们提出了一种名为\method的新型提示流水线，通过利用知识图谱（KGs）增强LLMs的推理能力与透明性。该方法使LLMs能够理解KG输入，并结合隐式知识与外部知识进行推理。此外，该方法揭示了LLMs的思维图谱，基于知识本体展现其推理路径。我们在多样化问答任务（尤其是医学领域）上评估了该方法，结果表明其显著优于基线模型。我们还引入了一个新的幻觉评估基准，并分析了方法不同组件的效果。实验结果验证了该方法在融合LLMs与KGs知识以实现联合推理方面的有效性与鲁棒性。为复现结果并进一步扩展框架，我们将代码库公开于https://github.com/wyl-willing/MindMap。