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Making Large Language Models Perform Better in Knowledge Graph Completion
April 16, 2024, 4:51 a.m. | Yichi Zhang, Zhuo Chen, Lingbing Guo, Yajing Xu, Wen Zhang, Huajun Chen
cs.CL updates on arXiv.org arxiv.org
Abstract: Large language model (LLM) based knowledge graph completion (KGC) aims to predict the missing triples in the KGs with LLMs. However, research about LLM-based KGC fails to sufficiently harness LLMs' inference proficiencies, overlooking critical structural information integral to KGs. In this paper, we explore methods to incorporate structural information into the LLMs, with the overarching goal of facilitating structure-aware reasoning. We first discuss on the existing LLM paradigms like in-context learning and instruction tuning, proposing …
arxiv cs.cl graph knowledge knowledge graph language language models large language large language models making type
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