# Augmenting LLMs with Knowledge: A survey on hallucination prevention

**Authors:** Konstantinos Andriopoulos, Johan Pouwelse  
**arXiv:** [2309.16459](https://arxiv.org/abs/2309.16459)  
**Date:** 2023-09-28  
**Summary page:** https://www.summarizepaper.com/en/paper/2309.16459v1/

## Key points

- Challenges and limitations faced by augmented large language models
- Evolving landscape of language generation and critical need for innovative solutions
- Enriching Language Models (LMs) with external knowledge to generate contextually grounded responses
- Integration of non-parametric modules leading to augmented language models
- Promise in reducing hallucinations and enhancing context, but facing limitations such as conflicting retrievals
- Limited exploration of the interplay between reasoning augmentation and knowledge integration
- Immense potential in advancing deep learning systems for complex human-machine interactions while minimizing parameter footprint

## Summary

In this comprehensive survey, we explore the challenges and limitations faced by augmented large language models. We emphasize the evolving landscape of language generation and stress the critical need for innovative solutions. By examining a wide range of works that enrich Language Models (LMs) with external knowledge, we witness how these models can generate contextually grounded and up-to-date responses. Through the integration of non-parametric modules, these augmented LMs depart from traditional language modeling paradigms and are categorized as augmented language models. While these augmented LMs show promise in reducing hallucinations and incorporating relevant information to enhance context, they still face limitations. Instances of conflicting retrievals leading to mixed answers highlight the ongoing need for refinement in this domain. Furthermore, there is limited exploration of the interplay between reasoning augmentation and knowledge integration, signaling a promising avenue for future research endeavors. Despite these challenges, the field of augmented language models holds immense potential and excitement. It represents a crucial step towards advancing deep learning systems capable of engaging in complex human-machine interactions while minimizing parameter footprint. As we reflect on the progress made in this field, it becomes evident that opportunities for further innovation and investigation abound for those shaping the future of this dynamic domain.