Large language model (LLM) has achieved outstanding performance on various downstream tasks with its powerful natural language understanding and zero-shot capability, but LLM still suffers from knowledge limitation. Especially in scenarios that require long logical chains or complex reasoning, the hallucination and knowledge limitation of LLM limit its performance in question answering (QA). In this paper, we propose a novel framework KnowledgeNavigator to address these challenges by efficiently and accurately retrieving external knowledge from knowledge graph and using it as a key factor to enhance LLM reasoning. Specifically, KnowledgeNavigator first mines and enhances the potential constraints of the given question to guide the reasoning. Then it retrieves and filters external knowledge that supports answering through iterative reasoning on knowledge graph with the guidance of LLM and the question. Finally, KnowledgeNavigator constructs the structured knowledge into effective prompts that are friendly to LLM to help its reasoning. We evaluate KnowledgeNavigator on multiple public KGQA benchmarks, the experiments show the framework has great effectiveness and generalization, outperforming previous knowledge graph enhanced LLM methods and is comparable to the fully supervised models.

翻译：大语言模型凭借其强大的自然语言理解能力和零样本能力，在下游各项任务中取得了卓越表现，但仍受限于知识不足的问题。尤其在需要长逻辑链或复杂推理的场景中，大语言模型的幻觉现象与知识局限性制约了其在问答任务中的性能。本文提出新颖框架KnowledgeNavigator，通过高效精准地从知识图谱中检索外部知识，并将其作为增强大语言模型推理能力的关键要素，有效应对上述挑战。具体而言，KnowledgeNavigator首先挖掘并增强给定问题中的潜在约束条件以引导推理过程，随后在大语言模型与问题的协同引导下，通过对知识图谱的迭代推理检索并过滤支持答案的外部知识。最后，KnowledgeNavigator将结构化知识构建为对大语言模型友好的有效提示，辅助其推理。我们在多个公开知识图谱问答基准上评估了KnowledgeNavigator，实验结果表明该框架具备卓越的有效性与泛化能力，超越了以往基于知识图谱增强的大语言模型方法，性能可与全监督模型相媲美。