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.

利用大型语言模型 (LLMs) 和知识图谱 (KG) 的协作训练方法，本研究在知识图谱的指导下，通过迭代探索和选择性地检索任务相关的知识子图来支持推理，使得 LLMs 能更可靠地进行基于知识的推理，并同时明确阐述推理过程，实验结果表明本方法在多个数据集上显著改进，尤其在 QALD10 数据集上相对于最佳基线和最先进的方法提高了超过 10%，为今后的知识图谱和大型语言模型融合研究提供了有价值的参考，提升 LLMs 在解决复杂问题上的能力。