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.

翻译：近期，大型语言模型（LLMs）展现出卓越的能力。然而，更新其知识仍面临挑战，当面对陌生问题时可能导致不准确。尽管已有研究将知识图谱与LLMs结合，但现有方法将LLM作为主要决策者，对其能力提出了高要求。这对于计算成本较低、性能相对较弱的LLMs尤其不适用。本文提出一种线索引导路径探索框架（CGPE），该框架高效融合知识库与LLM，且对模型能力要求较低。受人类手动检索知识方法的启发，CGPE利用问题中的信息作为线索，系统性地探索知识库中所需的路径。在开源数据集上的实验表明，CGPE优于先前方法，且可高效应用于参数较少的LLMs。在某些案例中，甚至拥有60亿参数的ChatGLM3也能媲美GPT-4的性能。此外，结果还显示CGPE对LLMs的调用频率极低，表明其计算开销减少。对于面临计算资源约束的组织和个人，本研究具有重要的实际价值。