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related 0.50 — strongly supporting 5 facts
Large Language Models are utilized to enhance knowledge graph completion through specific methodologies like 'KC-GENRE' [1] and by aiding in downstream tasks such as link prediction [2]. Research explores how these models blend memorized knowledge with inference during the completion process [3], with dedicated benchmarks [4] and academic literature [5] documenting their performance in this domain.
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Practices, opportunities and challenges in the fusion of knowledge ... frontiersin.org 3 facts
referenceThe paper 'Making large language models perform better in knowledge graph completion' was published as an arXiv preprint in 2023.
claimLarge Language Models (LLMs) intrinsically blend memorized knowledge with inferred predictions during knowledge graph completion, making it difficult to distinguish between the two.
referenceWang et al. (2024) introduced 'KC-GENRE', a knowledge-constrained generative re-ranking method based on large language models for knowledge graph completion.
A survey on augmenting knowledge graphs (KGs) with large ... link.springer.com 1 fact
claimBenchmarks like SimpleQuestions and FreebaseQA provide standardized datasets and evaluation metrics for consistent and comparative assessment of LLMs integrated with knowledge graphs, covering tasks such as natural language understanding, question answering, commonsense reasoning, and knowledge graph completion.
KG-IRAG: A Knowledge Graph-Based Iterative Retrieval-Augmented ... arxiv.org 1 fact
claimLarge Language Models (LLMs) contribute to knowledge graph completion, specifically aiding in downstream tasks such as node classification and link prediction.