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：大型语言模型凭借其强大的自然语言理解和零样本能力，在下游任务中取得了卓越性能，但仍受限于知识局限性。尤其在需要长逻辑链或复杂推理的场景中，其存在的幻觉问题和知识瓶颈制约了在问答任务中的表现。本文提出新型框架KnowledgeNavigator，通过高效准确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的关键要素来解决上述挑战。具体而言，该框架首先挖掘并增强给定问题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引导下对知识图谱进行迭代推理，检索并过滤辅助作答的外部知识。最后将结构化知识构建为大语言模型友好的有效提示以辅助推理。我们在多个公开KGQA基准上对KnowledgeNavigator进行评测，实验结果表明该框架具备卓越的有效性与泛化能力，优于现有知识图谱增强大语言模型方法，可媲美全监督模型。