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

Authors

Zihan Zhang, University of Technology Sydney
Meng Fang, University of Liverpool
Ling Chen, University of Technology Sydney
Mohammad Reza Namazi-Rad, University of Wollongong
Jun Wang, University College London

Publication Name

EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings

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Although large language models (LLMs) are impressive in solving various tasks, they can quickly be outdated after deployment.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 LLMs with the ever-changing world knowledge without re-training from scratch. We categorize research works systemically and provide in-depth comparisons and discussion. We also discuss existing challenges and highlight future directions to facilitate research in this field.

Open Access Status

This publication is not available as open access

First Page

8289

Last Page

8311

Funding Sponsor

France Télécom