While Large Language Models (LLMs) demonstrate exceptional performance in a multitude of Natural Language Processing (NLP) tasks, they encounter challenges in practical applications, including issues with hallucinations, inadequate knowledge updating, and limited transparency in the reasoning process. To overcome these limitations, this study innovatively proposes a collaborative training-free reasoning scheme involving tight cooperation between Knowledge Graph (KG) and LLMs. This scheme first involves using LLMs to iteratively explore KG, selectively retrieving a task-relevant knowledge subgraph to support reasoning. The LLMs are then guided to further combine inherent implicit knowledge to reason on the subgraph while explicitly elucidating the reasoning process. Through such a cooperative approach, our scheme achieves more reliable knowledge-based reasoning and facilitates the tracing of the reasoning results. Experimental results show that our scheme significantly progressed across multiple datasets, notably achieving over a 10% improvement on the QALD10 dataset compared to the best baseline and the fine-tuned state-of-the-art (SOTA) work. Building on this success, this study hopes to offer a valuable reference for future research in the fusion of KG and LLMs, thereby enhancing LLMs' proficiency in solving complex issues.

翻译：尽管大型语言模型（LLMs）在众多自然语言处理（NLP）任务中展现出卓越性能，但在实际应用中仍面临挑战，包括幻觉现象、知识更新不足以及推理过程透明度有限等问题。为突破这些局限，本研究创新性地提出一种无需训练的协同推理方案，通过知识图谱（KG）与LLMs的紧密协作实现推理。该方案首先利用LLMs迭代探索KG，选择性检索与任务相关的知识子图以支持推理；进而引导LLMs在子图上进一步结合其固有的隐性知识进行推理，同时显式阐明推理过程。通过这种协作方式，本方案实现了更可靠的知识驱动推理，并便于追溯推理结果。实验结果表明，该方案在多个数据集上取得显著进展，尤其在QALD10数据集上相比最优基线方法和微调后的最新技术（SOTA）工作实现了超过10%的性能提升。基于此成果，本研究期望为未来KG与LLMs融合研究提供宝贵参考，从而增强LLMs解决复杂问题的能力。