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In recent times, large language models (LLMs) have showcased remarkablecapabilities. However, updating their knowledge poses challenges, potentiallyleading to inaccuracies when confronted with unfamiliar queries. Whileintegrating knowledge graphs with LLMs has been explored, existing approachestreat LLMs as primary decision-makers, imposing high demands on theircapabilities. This is particularly unsuitable for LLMs with lower computationalcosts and relatively poorer performance. In this paper, we introduce aClue-Guided Path Exploration framework (CGPE) that efficiently merges aknowledge base with an LLM, placing less stringent requirements on the model'scapabilities. Inspired by the method humans use to manually retrieve knowledge,CGPE employs information from the question as clues to systematically explorethe required knowledge path within the knowledge base. Experiments onopen-source datasets reveal that CGPE outperforms previous methods and ishighly applicable to LLMs with fewer parameters. In some instances, evenChatGLM3, with its 6 billion parameters, can rival the performance of GPT-4.Furthermore, the results indicate a minimal invocation frequency of CGPE onLLMs, suggesting reduced computational overhead. For organizations andindividuals facing constraints in computational resources, our research offerssignificant practical value.

Clue-Guided Path Exploration: An Efficient Knowledge Base Question-Answering Framework with Low Computational Resource Consumption