External Knowledge Integration in Large Language Models: A Survey on Methods, Challenges, and Future Directions

TL;DR

A comprehensive survey on methods for integrating external knowledge into LLMs to overcome hallucinations and knowledge limitations.

Problem

LLMs suffer from hallucinations, outdated knowledge, and lack of domain-specific expertise due to training data limitations. This hinders their performance in knowledge-intensive fields like science, medicine, and law.

Approach

This survey focuses on decoder-based autoregressive LLMs and analyzes two main approaches: parametric (fine-tuning, knowledge distillation) and non-parametric (retrieval-augmented generation, external memory). It compares their strengths, weaknesses, and applications, and discusses potential for improving explainability and trustworthiness.

Results & Contribution

Systematically demonstrates that external knowledge integration significantly enhances reasoning, factual accuracy, and domain adaptability. Provides practical guidelines for developers and researchers, and outlines future research directions.