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Personalizing Large Language Models using Retrieval Augmented Generation and Knowledge Graph

A paper proposing an approach to personalize large language models using retrieval augmented generation with knowledge graphs.

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Personalizing Large Language Models using Retrieval Augmented Generation and Knowledge Graph

By Deeksha Prahlad, Chanhee Lee, Dongha Kim, Hokeun Kim
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The authors propose a method to address over-fitting in large language models by incorporating knowledge graphs for personalized response generation. They use calendar data as an example of frequently updated personal information.

Experimental results show improved accuracy and response time compared to baseline LLMs.

Abstract

The authors propose a method to address over-fitting in large language models by incorporating knowledge graphs for personalized response generation. They use calendar data as an example of frequently updated personal information. Experimental results show improved accuracy and response time compared to baseline LLMs.

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personalizationlanguage modelsknowledge graphsretrieval augmented generationresponse generationKnowledge GraphsLarge Language ModelsRetrieval & RAGSemantic Interoperability
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Personalizing Large Language Models using Retrieval Augmented Generation and Knowledge Graph | Aramai