Large language models (LLMs) have demonstrated impressive reasoning abilities
in complex tasks. However, they lack up-to-date knowledge and experience
hallucinations during reasoning, which can lead to incorrect reasoning
processes and diminish their performance and trustworthiness. Knowledge graphs
(KGs), which capture vast amounts of facts in a structured format, offer a
reliable source of knowledge for reasoning. Nevertheless, existing KG-based LLM
reasoning methods only treat KGs as factual knowledge bases and overlook the
importance of their structural information for reasoning. In this paper, we
propose a novel method called reasoning on graphs (RoG) that synergizes LLMs
with KGs to enable faithful and interpretable reasoning. Specifically, we
present a planning-retrieval-reasoning framework, where RoG first generates
relation paths grounded by KGs as faithful plans. These plans are then used to
retrieve valid reasoning paths from the KGs for LLMs to conduct faithful
reasoning. Furthermore, RoG not only distills knowledge from KGs to improve the
reasoning ability of LLMs through training but also allows seamless integration
with any arbitrary LLMs during inference. Extensive experiments on two
benchmark KGQA datasets demonstrate that RoG achieves state-of-the-art
performance on KG reasoning tasks and generates faithful and interpretable
reasoning results.

本论文提出了一种理论上基于知识图谱的新方法 —— 图推理（RoG），该方法通过将大型语言模型（LLMs）与知识图谱（KGs）相结合，实现了忠实和可解释的推理，实验结果表明 RoG 在 KG 推理任务上取得了最先进的性能。