@inproceedings{zhang-etal-2023-large,
title = "How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances",
author = "Zhang, Zihan and
Fang, Meng and
Chen, Ling and
Namazi-Rad, Mohammad-Reza and
Wang, Jun",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.516",
doi = "10.18653/v1/2023.emnlp-main.516",
pages = "8289--8311",
abstract = "Although large language models (LLMs) are impressive in solving various tasks, they can quickly be outdated after deployment.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.",
}
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<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Although large language models (LLMs) are impressive in solving various tasks, they can quickly be outdated after deployment.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.</abstract>
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%0 Conference Proceedings
%T How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances
%A Zhang, Zihan
%A Fang, Meng
%A Chen, Ling
%A Namazi-Rad, Mohammad-Reza
%A Wang, Jun
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F zhang-etal-2023-large
%X Although large language models (LLMs) are impressive in solving various tasks, they can quickly be outdated after deployment.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.
%R 10.18653/v1/2023.emnlp-main.516
%U https://aclanthology.org/2023.emnlp-main.516
%U https://doi.org/10.18653/v1/2023.emnlp-main.516
%P 8289-8311
Markdown (Informal)
[How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances](https://aclanthology.org/2023.emnlp-main.516) (Zhang et al., EMNLP 2023)
ACL