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http://purl.org/dc/terms/title
LLM as Prompter: Low-resource Inductive Reasoning on Arbitrary Knowledge Graphs
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This method uses Llama2-7B as a baseline for zero-shot graph reasoning by converting KG queries and subgraphs into textual questions. The LLM attempts to infer missing facts directly from the textual representation for KG completion.
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Llama2 for KG Reasoning
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PROLINK is a novel framework designed for low-resource inductive reasoning across arbitrary KGs. It utilizes a pre-trained LLM to generate a graph-structural prompt from relation semantics. This prompt graph is then calibrated and injected into the KG's relation graph to enhance a GNN-based reasoner, thereby improving the inference of missing facts (KG completion).
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LLM-KG assessment for paper 10.48550/arXiv.2402.11804
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