Deep reasoning of large models based on knowledge graph

Although Large Language Models (LLMs) have achieved significant success in various tasks, they often struggle with hallucination issues in scenarios requiring deep reasoning. Incorporating external knowledge into LLM reasoning can alleviate some of these issues. This paper proposes an efficient graph search method that integrates knowledge graphs. A knowledge graph is firstly constructed from the original document. This graph is then partitioned into multiple relevant communities using a community detection algorithm, and natural language summaries for each community are generated using an LLM. During the search phase, the LLM performs beam search on the knowledge graph, iteratively exploring multiple potential reasoning paths on the graph until it determines that it can answer the question based on the current reasoning path. Experiments demonstrate that our search method has lower computational costs and better generalizability, outperforming even large LLMs like GPT-4 in certain vertical domains.