A Comprehensive Survey on Integrating Large Language Models with Knowledge-Based Methods

TL;DR

This survey reviews how Large Language Models can be integrated with knowledge-based systems, highlighting current research, challenges, and future directions to enhance AI applications.

Contribution

It provides a comprehensive overview of methods, challenges, and solutions for combining LLMs with knowledge bases, identifying gaps and proposing future research paths.

Findings

Integration improves data contextualization and model accuracy.

Current solutions address technical and operational challenges.

The survey highlights key research gaps and future directions.

Abstract

The rapid development of artificial intelligence has led to marked progress in the field. One interesting direction for research is whether Large Language Models (LLMs) can be integrated with structured knowledge-based systems. This approach aims to combine the generative language understanding of LLMs and the precise knowledge representation systems by which they are integrated. This article surveys the relationship between LLMs and knowledge bases, looks at how they can be applied in practice, and discusses related technical, operational, and ethical challenges. Utilizing a comprehensive examination of the literature, the study both identifies important issues and assesses existing solutions. It demonstrates the merits of incorporating generative AI into structured knowledge-base systems concerning data contextualization, model accuracy, and utilization of knowledge resources. The findings give a full list of the current situation of research, point out the main gaps, and propose helpful paths to take. These insights contribute to advancing AI technologies and support their practical deployment across various sectors.