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

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

Computation and Language 2023-10-12 v1

Authors: 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Although large language models (LLMs) are impressive in solving various tasks, they can quickly be outdated after deployment.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 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. We release the paper list at https://github.com/hyintell/awesome-refreshing-llms

Keywords

large language models world knowledge knowledge updating model alignment survey

Cite

@article{arxiv.2310.07343,
  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},
  journal= {arXiv preprint arXiv:2310.07343},
  year   = {2023}
}

Comments

EMNLP 2023 main conference, paper link at https://github.com/hyintell/awesome-refreshing-llms

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

Abstract

Keywords

Cite

Comments

Related papers