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Large Language Models (LLMs) have shown impressive capabilities, yet updatingtheir knowledge remains a significant challenge, often leading to outdated orinaccurate responses. A proposed solution is the integration of externalknowledge bases, such as knowledge graphs, with LLMs. Most existing methods usea paradigm that treats the question as the objective, with relevant knowledgebeing incrementally retrieved from the knowledge graph. However, this strategyfrequently experiences an mismatch in the granularity of knowledge between thetarget question and the entities and relations being retrieved. As a result,the information in the question cannot precisely correspond to the retrievedknowledge. This may cause redundant exploration or omission of vital knowledge,thereby leading to enhanced computational consumption and reduced retrievalaccuracy. In this paper, we propose a novel paradigm of fine-grained statefulknowledge exploration, which addresses the `information granularity mismatch'issue. We extract fine-grained information from questions and explore thesemantic mapping between this information and the knowledge in graph. Bydynamically updating the mapping records, we avoid redundant exploration andensure no pertinent information is overlooked, thereby reducing computationaloverhead and improving the accuracy of knowledge exploration. The use offine-grained information also eliminates the need for a priori knowledge, acommon requirement in existing methods. Experiments on multiple datasetsrevealed that our paradigm surpasses current advanced methods in knowledgeretrieval while significantly reducing the average number of LLM invocations.

Fine-Grained Stateful Knowledge Exploration: A Novel Paradigm for Integrating Knowledge Graphs with Large Language Models