How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances

Zihan Zhang; Meng Fang; Ling Chen; Mohammad Reza Namazi Rad; Jun Wang

EMNLP 2023long main0 citations

How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances

Zihan Zhang, Meng Fang, Ling Chen, Mohammad Reza Namazi Rad, Jun Wang

Abstract

Although large language models (LLMs) are impressive in solving various tasks, they can quickly be outdated after deployment. Maintaining their up-to-date status is a pressing concern in the current era. This paper provides a comprehensive review of recent advances in aligning deployed LLMs with the ever-changing world knowledge. We categorize research works systemically and provide in-depth comparisons and discussions. We also discuss existing challenges and highlight future directions to facilitate research in this field.

large language modelssurveyknowledge

BibTeX

@inproceedings{
zhang2023how,
title={How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances},
author={Zihan Zhang and Meng Fang and Ling Chen and Mohammad Reza Namazi Rad and Jun Wang},
booktitle={The 2023 Conference on Empirical Methods in Natural Language Processing},
year={2023},
url={https://openreview.net/forum?id=UyLaqZ6PHA}
}