Large language models (LLMs) have demonstrated remarkable capabilities in various complex tasks, yet they still suffer from hallucinations. Introducing external knowledge, such as knowledge graph, can enhance the LLMs' ability to provide factual answers. LLMs have the ability to interactively explore knowledge graphs. However, most approaches have been affected by insufficient internal knowledge excavation in LLMs, limited generation of trustworthy knowledge reasoning paths, and a vague integration between internal and external knowledge. Therefore, we propose KnowPath, a knowledge-enhanced large model framework driven by the collaboration of internal and external knowledge. It relies on the internal knowledge of the LLM to guide the exploration of interpretable directed subgraphs in external knowledge graphs, better integrating the two knowledge sources for more accurate reasoning. Extensive experiments on multiple real-world datasets confirm the superiority of KnowPath.
View on arXiv@article{zhao2025_2502.12029,
title={ KnowPath: Knowledge-enhanced Reasoning via LLM-generated Inference Paths over Knowledge Graphs },
author={ Qi Zhao and Hongyu Yang and Qi Song and Xinwei Yao and Xiangyang Li },
journal={arXiv preprint arXiv:2502.12029},
year={ 2025 }
}