KnowledgeNavigator: Leveraging Large Language Models for Enhanced Reasoning over Knowledge Graph
This addresses the challenge of improving question answering accuracy in scenarios requiring long logical chains, though it is incremental as it builds on existing knowledge graph-enhanced LLM methods.
The paper tackles the problem of knowledge limitation and hallucination in large language models during complex reasoning tasks by proposing KnowledgeNavigator, a framework that retrieves and integrates external knowledge from knowledge graphs to enhance reasoning, achieving performance comparable to fully supervised models on multiple KGQA benchmarks.
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.