Abstract: Large language models (LLMs) acquire various abilities and knowledge through a large amount of data, but still have problems such as illusions and lack of specialized domain knowledge, which can be mitigated by introducing an external knowledge graph. A new method called global pruning retrieval (GPR) is proposed for knowledge acquisition from knowledge graphs, which retrieves relevant relations and entities through breadth first search (BFS) and prunes to extract highly relevant relations and entities with a global perspective. At the same time, the entities in the question are connected by the shortest path to the relations. The relations and entities are transformed into prompt and pushed to LLMs, which guide LLMs to reason and generate answers and textualize the reasoning process, making the decision transparent and traceable. Experimental results on multiple datasets show that GPR has a better reasoning advantage, and the retrieved knowledge can better alleviate the illusion and domain knowledge deficit problems of LLMs.

Key words: knowledge graph, large language model, synergistic enhancement, information retrieval