Personalizing Large Language Models using Retrieval Augmented Generation and Knowledge Graph

TL;DR

This paper introduces a retrieval augmented generation method using knowledge graphs to improve personalized, factual response generation in large language models, reducing hallucinations and enhancing accuracy.

Contribution

The paper presents a novel integration of knowledge graphs with LLMs for personalized response generation, focusing on calendar data to improve factual accuracy.

Findings

Significantly better understanding of personal information.

More accurate responses compared to baseline LLMs.

Moderate reduction in response time.

Abstract

The advent of large language models (LLMs) has allowed numerous applications, including the generation of queried responses, to be leveraged in chatbots and other conversational assistants. Being trained on a plethora of data, LLMs often undergo high levels of over-fitting, resulting in the generation of extra and incorrect data, thus causing hallucinations in output generation. One of the root causes of such problems is the lack of timely, factual, and personalized information fed to the LLM. In this paper, we propose an approach to address these problems by introducing retrieval augmented generation (RAG) using knowledge graphs (KGs) to assist the LLM in personalized response generation tailored to the users. KGs have the advantage of storing continuously updated factual information in a structured way. While our KGs can be used for a variety of frequently updated personal data, such as calendar, contact, and location data, we focus on calendar data in this paper. Our experimental results show that our approach works significantly better in understanding personal information and generating accurate responses compared to the baseline LLMs using personal data as text inputs, with a moderate reduction in response time.