The task of predicting multiple links within knowledge graphs (KGs) stands as
a challenge in the field of knowledge graph analysis, a challenge increasingly
resolvable due to advancements in natural language processing (NLP) and KG
embedding techniques. This paper introduces a novel methodology, the Knowledge
Graph Large Language Model Framework (KG-LLM), which leverages pivotal NLP
paradigms, including chain-of-thought (CoT) prompting and in-context learning
(ICL), to enhance multi-hop link prediction in KGs. By converting the KG to a
CoT prompt, our framework is designed to discern and learn the latent
representations of entities and their interrelations. To show the efficacy of
the KG-LLM Framework, we fine-tune three leading Large Language Models (LLMs)
within this framework, employing both non-ICL and ICL tasks for a comprehensive
evaluation. Further, we explore the framework's potential to provide LLMs with
zero-shot capabilities for handling previously unseen prompts. Our experimental
findings discover that integrating ICL and CoT not only augments the
performance of our approach but also significantly boosts the models'
generalization capacity, thereby ensuring more precise predictions in
unfamiliar scenarios.

通过使用链式思维促进和上下文学习，该研究介绍了一种新方法，即知识图大型语言模型框架（KG-LLM），以提高知识图中多跳链接预测的效果。实验证明，集成上下文学习和链式思维不仅增加了方法的性能，而且显著提高了模型的泛化能力，从而在陌生情境中实现更准确的预测。