  In recent times, large language models (LLMs) have showcased remarkable
capabilities. However, updating their knowledge poses challenges, potentially
leading to inaccuracies when confronted with unfamiliar queries. While
integrating knowledge graphs with LLMs has been explored, existing approaches
treat LLMs as primary decision-makers, imposing high demands on their
capabilities. This is particularly unsuitable for LLMs with lower computational
costs and relatively poorer performance. In this paper, we introduce a
Clue-Guided Path Exploration framework (CGPE) that efficiently merges a
knowledge base with an LLM, placing less stringent requirements on the model's
capabilities. Inspired by the method humans use to manually retrieve knowledge,
CGPE employs information from the question as clues to systematically explore
the required knowledge path within the knowledge base. Experiments on
open-source datasets reveal that CGPE outperforms previous methods and is
highly applicable to LLMs with fewer parameters. In some instances, even
ChatGLM3, with its 6 billion parameters, can rival the performance of GPT-4.
Furthermore, the results indicate a minimal invocation frequency of CGPE on
LLMs, suggesting reduced computational overhead. For organizations and
individuals facing constraints in computational resources, our research offers
significant practical value.
