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Leveraging LLM based Retrieval-Augmented Generation for Legal Knowledge Graph Completion

A paper proposing a model for legal knowledge graph completion using Large Language Models and retrieval-augmented generation.

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Leveraging LLM based Retrieval-Augmented Generation for Legal Knowledge Graph Completion

By Lai Jiang, Zheng Conghui, Zhang Xiaohan, Sun Fuhui, Xiaoyan Wang, Li Pan
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The authors propose RA-KG-LLM, a model that combines the generative capabilities of Large Language Models with retrieval-augmented technology to enhance semantic information and interrelations mining in knowledge graphs.

The model is evaluated on five real datasets, including Cail2022, where it achieves better performance than state-of-the-art related works. This paper aims to improve the completeness and accuracy of legal knowledge graphs.

Abstract

The authors propose RA-KG-LLM, a model that combines the generative capabilities of Large Language Models with retrieval-augmented technology to enhance semantic information and interrelations mining in knowledge graphs. The model is evaluated on five real datasets, including Cail2022, where it achieves better performance than state-of-the-art related works. This paper aims to improve the completeness and accuracy of legal knowledge graphs.

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knowledge graph completionlegal domainlarge language modelsretrieval-augmented generationKnowledge GraphsLarge Language ModelsRetrieval & RAGSemantic Interoperability
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