June 4, 2024, 4:53 a.m. | Guangyi Liu, Yongqi Zhang, Yong Li, Quanming Yao

cs.CL updates on arXiv.org arxiv.org

arXiv:2406.01145v1 Announce Type: new
Abstract: The task of reasoning over Knowledge Graphs (KGs) poses a significant challenge for Large Language Models (LLMs) due to the complex structure and large amounts of irrelevant information. Existing LLM reasoning methods overlook the importance of compositional learning on KG to supply with precise knowledge. Besides, the fine-tuning and frequent interaction with LLMs incur substantial time and resource costs. This paper focuses on the Question Answering over Knowledge Graph (KGQA) task and proposes an Explore-then-Determine …

abstract arxiv challenge cs.cl explore framework gnn graph graphs importance information knowledge knowledge graph knowledge graphs language language models large language large language models llm llm reasoning llms reasoning synergy type

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